La ville de Seattle s'intéresse de près aux émissions
des bâtiments non destinés à l'habitation.
Les agents de la ville ont réalisé des relevés minutieux
dans les bâtiments de la ville en 2015 et en 2016.
Cependant, les relevés sont couteux à obtenir.
Est-il possible de prédire, et donc de se passer, des relevés des agents
pour les années à venir ?
Et donc de calculer les émissions de CO2 et la consommation totale d'énergie
des bâtiments non destinés à l'habitation, en s'appuyant uniquement
sur les relevés déjà effectués en 2015 et 2016 ?
Tenter de prédire les émissions de CO2 et la consommation totale d'énergie
de nouveaux bâtiments en s'appuyant sur les relevés des agents de la ville
de Seattle en 2015 et 2016.
L'idée est d'explorer ces données, de les nettoyer, de les comprendre
et de les optimiser afin d'être utilisées à travers des algorithmes de Machine Learning.
Nous verrons en détail chacune de ces étapes.
Nous testerons, nous comparerons et nous optimiserons
différents Algorithmes de Machine Learning pour enfin conclure
sur la question qui nous est posée : Peut-on prédire efficacement les émissions de CO2
et la consommation totale d'énergie pour les années à venir sans devoir réaliser de
nouveaux relevés par nos agents de terrain ?
Nous chercherons également à évaluer l'intérêt de l'**ENERGYSTARScore** pour la prédiction d'émissions.
En effet, si cette donnée peut sembler très pertinente,
elle a le désavantage d'être fastidieuse, et donc couteuse, à calculer.
En d'autres termes, nous répondrons à la question : Est-il rentable d'utiliser
l'ENERGYSTARScore pour effectuer nos prédictions en émissions ?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
from scipy import stats
from time import perf_counter
from sklearn.preprocessing import StandardScaler, OneHotEncoder, MultiLabelBinarizer, OrdinalEncoder, LabelEncoder
from sklearn.feature_selection import SelectKBest, RFE, RFECV, SelectFromModel, mutual_info_regression, f_regression
from sklearn.model_selection import train_test_split, cross_val_score, validation_curve, GridSearchCV, RandomizedSearchCV, learning_curve
from sklearn.linear_model import LinearRegression, Ridge, Lasso, LassoCV, ElasticNetCV
from sklearn.neighbors import KNeighborsRegressor
from sklearn.svm import LinearSVR, SVR
from sklearn.kernel_ridge import KernelRidge
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor, AdaBoostRegressor
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
from sklearn.dummy import DummyRegressor
from sklearn.decomposition import PCA
# !pip install yellowbrick
from yellowbrick.regressor import CooksDistance
Définition du **R**andom_**S**tate pour la reproductibilité des résultats :
J'utilise dans ce projet un random_state à 1 mais je laisse le choix
à l'utilisateur de le changer facilement s'il le souhaite afin d'observer
l'influence que celà peut avoir sur les scores obtenus.
Le même Random_State sera défini dans l'ensemble
des processus du projet (où il est possible de l'utiliser)
RS = 1
Modification des paramètres de Pandas concernant
le nombre de lignes et colonnes pouvant être affichées
ainsi que le format d'affichage des nombres réels :
pd.set_option("max_rows", 300)
pd.set_option("display.max_columns",200)
pd.set_option('display.float_format', lambda x: '%.5f' % x)
df15 = pd.read_csv('2015-building-energy-benchmarking.csv', sep=',')
df15.head()
| OSEBuildingID | DataYear | BuildingType | PrimaryPropertyType | PropertyName | TaxParcelIdentificationNumber | Location | CouncilDistrictCode | Neighborhood | YearBuilt | NumberofBuildings | NumberofFloors | PropertyGFATotal | PropertyGFAParking | PropertyGFABuilding(s) | ListOfAllPropertyUseTypes | LargestPropertyUseType | LargestPropertyUseTypeGFA | SecondLargestPropertyUseType | SecondLargestPropertyUseTypeGFA | ThirdLargestPropertyUseType | ThirdLargestPropertyUseTypeGFA | YearsENERGYSTARCertified | ENERGYSTARScore | SiteEUI(kBtu/sf) | SiteEUIWN(kBtu/sf) | SourceEUI(kBtu/sf) | SourceEUIWN(kBtu/sf) | SiteEnergyUse(kBtu) | SiteEnergyUseWN(kBtu) | SteamUse(kBtu) | Electricity(kWh) | Electricity(kBtu) | NaturalGas(therms) | NaturalGas(kBtu) | OtherFuelUse(kBtu) | GHGEmissions(MetricTonsCO2e) | GHGEmissionsIntensity(kgCO2e/ft2) | DefaultData | Comment | ComplianceStatus | Outlier | 2010 Census Tracts | Seattle Police Department Micro Community Policing Plan Areas | City Council Districts | SPD Beats | Zip Codes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2015 | NonResidential | Hotel | MAYFLOWER PARK HOTEL | 659000030 | {'latitude': '47.61219025', 'longitude': '-122... | 7 | DOWNTOWN | 1927 | 1 | 12.00000 | 88434 | 0 | 88434 | Hotel | Hotel | 88434.00000 | NaN | nan | NaN | nan | NaN | 65.00000 | 78.90000 | 80.30000 | 173.50000 | 175.10000 | 6981428.00000 | 7097539.00000 | 2023032.00000 | 1080307.00000 | 3686160.00000 | 12724.00000 | 1272388.00000 | 0.00000 | 249.43000 | 2.64000 | No | NaN | Compliant | NaN | nan | 14.00000 | nan | 31.00000 | 18081 |
| 1 | 2 | 2015 | NonResidential | Hotel | PARAMOUNT HOTEL | 659000220 | {'latitude': '47.61310583', 'longitude': '-122... | 7 | DOWNTOWN | 1996 | 1 | 11.00000 | 103566 | 15064 | 88502 | Hotel, Parking, Restaurant | Hotel | 83880.00000 | Parking | 15064.00000 | Restaurant | 4622.00000 | NaN | 51.00000 | 94.40000 | 99.00000 | 191.30000 | 195.20000 | 8354235.00000 | 8765788.00000 | 0.00000 | 1144563.00000 | 3905411.00000 | 44490.00000 | 4448985.00000 | 0.00000 | 263.51000 | 2.38000 | No | NaN | Compliant | NaN | nan | 14.00000 | nan | 31.00000 | 18081 |
| 2 | 3 | 2015 | NonResidential | Hotel | WESTIN HOTEL | 659000475 | {'latitude': '47.61334897', 'longitude': '-122... | 7 | DOWNTOWN | 1969 | 1 | 41.00000 | 961990 | 0 | 961990 | Hotel, Parking, Swimming Pool | Hotel | 757243.00000 | Parking | 100000.00000 | Swimming Pool | 0.00000 | NaN | 18.00000 | 96.60000 | 99.70000 | 242.70000 | 246.50000 | 73130656.00000 | 75506272.00000 | 19660404.00000 | 14583930.00000 | 49762435.00000 | 37099.00000 | 3709900.00000 | 0.00000 | 2061.48000 | 1.92000 | Yes | NaN | Compliant | NaN | nan | 56.00000 | nan | 31.00000 | 18081 |
| 3 | 5 | 2015 | NonResidential | Hotel | HOTEL MAX | 659000640 | {'latitude': '47.61421585', 'longitude': '-122... | 7 | DOWNTOWN | 1926 | 1 | 10.00000 | 61320 | 0 | 61320 | Hotel | Hotel | 61320.00000 | NaN | nan | NaN | nan | NaN | 1.00000 | 460.40000 | 462.50000 | 636.30000 | 643.20000 | 28229320.00000 | 28363444.00000 | 23458518.00000 | 811521.00000 | 2769023.00000 | 20019.00000 | 2001894.00000 | 0.00000 | 1936.34000 | 31.38000 | No | NaN | Compliant | High Outlier | nan | 56.00000 | nan | 31.00000 | 18081 |
| 4 | 8 | 2015 | NonResidential | Hotel | WARWICK SEATTLE HOTEL | 659000970 | {'latitude': '47.6137544', 'longitude': '-122.... | 7 | DOWNTOWN | 1980 | 1 | 18.00000 | 119890 | 12460 | 107430 | Hotel, Parking, Swimming Pool | Hotel | 123445.00000 | Parking | 68009.00000 | Swimming Pool | 0.00000 | NaN | 67.00000 | 120.10000 | 122.10000 | 228.80000 | 227.10000 | 14829099.00000 | 15078243.00000 | 0.00000 | 1777841.00000 | 6066245.00000 | 87631.00000 | 8763105.00000 | 0.00000 | 507.70000 | 4.02000 | No | NaN | Compliant | NaN | nan | 56.00000 | nan | 31.00000 | 19576 |
df16 = pd.read_csv('2016-building-energy-benchmarking.csv', sep=',')
df16.head()
| OSEBuildingID | DataYear | BuildingType | PrimaryPropertyType | PropertyName | Address | City | State | ZipCode | TaxParcelIdentificationNumber | CouncilDistrictCode | Neighborhood | Latitude | Longitude | YearBuilt | NumberofBuildings | NumberofFloors | PropertyGFATotal | PropertyGFAParking | PropertyGFABuilding(s) | ListOfAllPropertyUseTypes | LargestPropertyUseType | LargestPropertyUseTypeGFA | SecondLargestPropertyUseType | SecondLargestPropertyUseTypeGFA | ThirdLargestPropertyUseType | ThirdLargestPropertyUseTypeGFA | YearsENERGYSTARCertified | ENERGYSTARScore | SiteEUI(kBtu/sf) | SiteEUIWN(kBtu/sf) | SourceEUI(kBtu/sf) | SourceEUIWN(kBtu/sf) | SiteEnergyUse(kBtu) | SiteEnergyUseWN(kBtu) | SteamUse(kBtu) | Electricity(kWh) | Electricity(kBtu) | NaturalGas(therms) | NaturalGas(kBtu) | DefaultData | Comments | ComplianceStatus | Outlier | TotalGHGEmissions | GHGEmissionsIntensity | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2016 | NonResidential | Hotel | Mayflower park hotel | 405 Olive way | Seattle | WA | 98101.00000 | 0659000030 | 7 | DOWNTOWN | 47.61220 | -122.33799 | 1927 | 1.00000 | 12 | 88434 | 0 | 88434 | Hotel | Hotel | 88434.00000 | NaN | nan | NaN | nan | NaN | 60.00000 | 81.70000 | 84.30000 | 182.50000 | 189.00000 | 7226362.50000 | 7456910.00000 | 2003882.00000 | 1156514.25000 | 3946027.00000 | 12764.52930 | 1276453.00000 | False | nan | Compliant | NaN | 249.98000 | 2.83000 |
| 1 | 2 | 2016 | NonResidential | Hotel | Paramount Hotel | 724 Pine street | Seattle | WA | 98101.00000 | 0659000220 | 7 | DOWNTOWN | 47.61317 | -122.33393 | 1996 | 1.00000 | 11 | 103566 | 15064 | 88502 | Hotel, Parking, Restaurant | Hotel | 83880.00000 | Parking | 15064.00000 | Restaurant | 4622.00000 | NaN | 61.00000 | 94.80000 | 97.90000 | 176.10001 | 179.39999 | 8387933.00000 | 8664479.00000 | 0.00000 | 950425.18750 | 3242851.00000 | 51450.81641 | 5145082.00000 | False | nan | Compliant | NaN | 295.86000 | 2.86000 |
| 2 | 3 | 2016 | NonResidential | Hotel | 5673-The Westin Seattle | 1900 5th Avenue | Seattle | WA | 98101.00000 | 0659000475 | 7 | DOWNTOWN | 47.61393 | -122.33810 | 1969 | 1.00000 | 41 | 956110 | 196718 | 759392 | Hotel | Hotel | 756493.00000 | NaN | nan | NaN | nan | NaN | 43.00000 | 96.00000 | 97.70000 | 241.89999 | 244.10001 | 72587024.00000 | 73937112.00000 | 21566554.00000 | 14515435.00000 | 49526664.00000 | 14938.00000 | 1493800.00000 | False | nan | Compliant | NaN | 2089.28000 | 2.19000 |
| 3 | 5 | 2016 | NonResidential | Hotel | HOTEL MAX | 620 STEWART ST | Seattle | WA | 98101.00000 | 0659000640 | 7 | DOWNTOWN | 47.61412 | -122.33664 | 1926 | 1.00000 | 10 | 61320 | 0 | 61320 | Hotel | Hotel | 61320.00000 | NaN | nan | NaN | nan | NaN | 56.00000 | 110.80000 | 113.30000 | 216.20000 | 224.00000 | 6794584.00000 | 6946800.50000 | 2214446.25000 | 811525.31250 | 2768924.00000 | 18112.13086 | 1811213.00000 | False | nan | Compliant | NaN | 286.43000 | 4.67000 |
| 4 | 8 | 2016 | NonResidential | Hotel | WARWICK SEATTLE HOTEL (ID8) | 401 LENORA ST | Seattle | WA | 98121.00000 | 0659000970 | 7 | DOWNTOWN | 47.61375 | -122.34047 | 1980 | 1.00000 | 18 | 175580 | 62000 | 113580 | Hotel, Parking, Swimming Pool | Hotel | 123445.00000 | Parking | 68009.00000 | Swimming Pool | 0.00000 | NaN | 75.00000 | 114.80000 | 118.70000 | 211.39999 | 215.60001 | 14172606.00000 | 14656503.00000 | 0.00000 | 1573448.62500 | 5368607.00000 | 88039.98438 | 8803998.00000 | False | nan | Compliant | NaN | 505.01000 | 2.88000 |
J'utilise une heatmap qui met en évidence les valeurs
manquantes en blanc et les valeurs renseignées en bleu foncé.
J'utiliserai cette heatmap régulièrement durant mon analyse.
plt.figure(figsize=(20,10))
sns.heatmap(df15.append(df16, sort=False, ignore_index=True).isna(), cbar=False)
<AxesSubplot:>
La colonne 'Location' contient des dictionnaires imbriqués.
Les clés de ces dictionnaires correspondent à des colonnes du DataFrame de 2016.
Je vais extraire ces données et renommer les colonnes afin de pouvoir merger
correctement les deux DataFrames.
A noter : La colonne 'Zip Codes' de df15 ne correspond pas à la colonne 'ZipCode' de df16.
La colonne contenant le Zip Code dans le df15 correspondant au Zip Code de df16 est
contenu dans la colonne 'Location' de df15.
La forme des données représente un dictionnaire imbriqué, encodé sous forme d'une *String* :
df15.Location[0]
'{\'latitude\': \'47.61219025\', \'longitude\': \'-122.33799744\', \'human_address\': \'{"address": "405 OLIVE WAY", "city": "SEATTLE", "state": "WA", "zip": "98101"}\'}'
import json
def LocationToDict(loc):
'''Retourne un dictionnaire contenu sous forme de String,
dans le format Dictionnaire'''
return json.loads(loc.replace("\'", "\"").replace("\"{","{").replace("}\"","}"))
def FromDictToSeries(dict):
''' Retourne le Dictionnaire de la colonne Location sous forme de Series Pandas
afin d'insérer facilement ces données au df15,
avec les colonnes directement et correctement renommées pour
correspondre aux colonnes correspondantes de df16'''
return pd.Series({'Address': dict['human_address']['address'],
'City':dict['human_address']['city'],
'State':dict['human_address']['state'],
'ZipCode':int(dict['human_address']['zip']),
'Latitude':float(dict['latitude']),
'Longitude':float(dict['longitude'])})
Extraction et Merge des colonnes issues des données encodées dans la colonne '**Location**' de *df15* à *df15* :
df15 = df15.merge(df15.Location.apply(lambda col: FromDictToSeries(LocationToDict(col))),
left_index=True,
right_index=True)
Suppression de la colonne '**Location**' :
df15.drop('Location', axis=1, inplace=True)
Je renomme mes **Targets** d'autres colonnes pour plus de lisibilité :
df15.rename(columns={"GHGEmissions(MetricTonsCO2e)": "TotalGHGEmissions",
"GHGEmissionsIntensity(kgCO2e/ft2)": "GHGEmissionsIntensity",
"Comment": "Comments"},
inplace=True)
Les noms des colonnes étant maintenant parfaitement alignés,
il est possible de merger correctement les 2 DataFrames.**'
df = df15.append(df16, sort=False, ignore_index=True)
Pour plus de lisibilité je renomme la colonne '**SiteEnergyUse(kBtu)**' sans son unité énergétique *kBtu*
ainsi que la colonne '**PropertyGFABuilding(s)**' sans ses *parenthèses* :
df.rename(columns={"SiteEnergyUse(kBtu)": "SiteEnergyUse",
"PropertyGFABuilding(s)": "PropertyGFABuilding"},
inplace=True)
Visualisation du nouveau DataFrame '**df**' :
plt.figure(figsize=(20,10))
sns.heatmap(df.isna(), cbar=False)
<AxesSubplot:>
df.head()
| OSEBuildingID | DataYear | BuildingType | PrimaryPropertyType | PropertyName | TaxParcelIdentificationNumber | CouncilDistrictCode | Neighborhood | YearBuilt | NumberofBuildings | NumberofFloors | PropertyGFATotal | PropertyGFAParking | PropertyGFABuilding | ListOfAllPropertyUseTypes | LargestPropertyUseType | LargestPropertyUseTypeGFA | SecondLargestPropertyUseType | SecondLargestPropertyUseTypeGFA | ThirdLargestPropertyUseType | ThirdLargestPropertyUseTypeGFA | YearsENERGYSTARCertified | ENERGYSTARScore | SiteEUI(kBtu/sf) | SiteEUIWN(kBtu/sf) | SourceEUI(kBtu/sf) | SourceEUIWN(kBtu/sf) | SiteEnergyUse | SiteEnergyUseWN(kBtu) | SteamUse(kBtu) | Electricity(kWh) | Electricity(kBtu) | NaturalGas(therms) | NaturalGas(kBtu) | OtherFuelUse(kBtu) | TotalGHGEmissions | GHGEmissionsIntensity | DefaultData | Comments | ComplianceStatus | Outlier | 2010 Census Tracts | Seattle Police Department Micro Community Policing Plan Areas | City Council Districts | SPD Beats | Zip Codes | Address | City | State | ZipCode | Latitude | Longitude | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2015 | NonResidential | Hotel | MAYFLOWER PARK HOTEL | 659000030 | 7 | DOWNTOWN | 1927 | 1.00000 | 12.00000 | 88434 | 0 | 88434 | Hotel | Hotel | 88434.00000 | NaN | nan | NaN | nan | NaN | 65.00000 | 78.90000 | 80.30000 | 173.50000 | 175.10000 | 6981428.00000 | 7097539.00000 | 2023032.00000 | 1080307.00000 | 3686160.00000 | 12724.00000 | 1272388.00000 | 0.00000 | 249.43000 | 2.64000 | No | NaN | Compliant | NaN | nan | 14.00000 | nan | 31.00000 | 18081.00000 | 405 OLIVE WAY | SEATTLE | WA | 98101.00000 | 47.61219 | -122.33800 |
| 1 | 2 | 2015 | NonResidential | Hotel | PARAMOUNT HOTEL | 659000220 | 7 | DOWNTOWN | 1996 | 1.00000 | 11.00000 | 103566 | 15064 | 88502 | Hotel, Parking, Restaurant | Hotel | 83880.00000 | Parking | 15064.00000 | Restaurant | 4622.00000 | NaN | 51.00000 | 94.40000 | 99.00000 | 191.30000 | 195.20000 | 8354235.00000 | 8765788.00000 | 0.00000 | 1144563.00000 | 3905411.00000 | 44490.00000 | 4448985.00000 | 0.00000 | 263.51000 | 2.38000 | No | NaN | Compliant | NaN | nan | 14.00000 | nan | 31.00000 | 18081.00000 | 724 PINE ST | SEATTLE | WA | 98101.00000 | 47.61311 | -122.33336 |
| 2 | 3 | 2015 | NonResidential | Hotel | WESTIN HOTEL | 659000475 | 7 | DOWNTOWN | 1969 | 1.00000 | 41.00000 | 961990 | 0 | 961990 | Hotel, Parking, Swimming Pool | Hotel | 757243.00000 | Parking | 100000.00000 | Swimming Pool | 0.00000 | NaN | 18.00000 | 96.60000 | 99.70000 | 242.70000 | 246.50000 | 73130656.00000 | 75506272.00000 | 19660404.00000 | 14583930.00000 | 49762435.00000 | 37099.00000 | 3709900.00000 | 0.00000 | 2061.48000 | 1.92000 | Yes | NaN | Compliant | NaN | nan | 56.00000 | nan | 31.00000 | 18081.00000 | 1900 5TH AVE | SEATTLE | WA | 98101.00000 | 47.61335 | -122.33770 |
| 3 | 5 | 2015 | NonResidential | Hotel | HOTEL MAX | 659000640 | 7 | DOWNTOWN | 1926 | 1.00000 | 10.00000 | 61320 | 0 | 61320 | Hotel | Hotel | 61320.00000 | NaN | nan | NaN | nan | NaN | 1.00000 | 460.40000 | 462.50000 | 636.30000 | 643.20000 | 28229320.00000 | 28363444.00000 | 23458518.00000 | 811521.00000 | 2769023.00000 | 20019.00000 | 2001894.00000 | 0.00000 | 1936.34000 | 31.38000 | No | NaN | Compliant | High Outlier | nan | 56.00000 | nan | 31.00000 | 18081.00000 | 620 STEWART ST | SEATTLE | WA | 98101.00000 | 47.61422 | -122.33661 |
| 4 | 8 | 2015 | NonResidential | Hotel | WARWICK SEATTLE HOTEL | 659000970 | 7 | DOWNTOWN | 1980 | 1.00000 | 18.00000 | 119890 | 12460 | 107430 | Hotel, Parking, Swimming Pool | Hotel | 123445.00000 | Parking | 68009.00000 | Swimming Pool | 0.00000 | NaN | 67.00000 | 120.10000 | 122.10000 | 228.80000 | 227.10000 | 14829099.00000 | 15078243.00000 | 0.00000 | 1777841.00000 | 6066245.00000 | 87631.00000 | 8763105.00000 | 0.00000 | 507.70000 | 4.02000 | No | NaN | Compliant | NaN | nan | 56.00000 | nan | 31.00000 | 19576.00000 | 401 LENORA ST | SEATTLE | WA | 98121.00000 | 47.61375 | -122.34092 |
Il me semble pertinent de remplacer les deux colonnes :
par une nouvelle colonne nommé 'ageBuilding' qui nous indique
depuis combien de temps un bâtiment à été construit.
Nous conservons autant d'informations avec moins de colonnes ce qui est apréciable,
autant d'un point de vue de la lisibilité, qu'en optimisation pour le Machine Learning.
df['ageBuilding'] = df['DataYear'] - df['YearBuilt']
df.drop(['DataYear','YearBuilt'], axis=1, inplace=True)
La Dataset recense 4 sources d'énergie différentes :
Je vais créer 4 nouvelles colonnes qui indiqueront, pour chaque bâtiment,
la proportion utilisée pour chaque énergie.
Au préalable :
df = df[(df['SiteEnergyUse'] > 0) & (df['SiteEnergyUse'].notnull())]
df[['SteamUse(kBtu)','Electricity(kBtu)','NaturalGas(kBtu)','OtherFuelUse(kBtu)']] = \
df[['SteamUse(kBtu)','Electricity(kBtu)','NaturalGas(kBtu)','OtherFuelUse(kBtu)']].fillna(0)
df['Total_EnergyUse'] = df['Electricity(kBtu)'] + df['NaturalGas(kBtu)'] + df['SteamUse(kBtu)'] + df['OtherFuelUse(kBtu)']
df[['SiteEnergyUse',
'Total_EnergyUse',
'SteamUse(kBtu)',
'Electricity(kBtu)',
'NaturalGas(kBtu)',
'OtherFuelUse(kBtu)']].head(30)
| SiteEnergyUse | Total_EnergyUse | SteamUse(kBtu) | Electricity(kBtu) | NaturalGas(kBtu) | OtherFuelUse(kBtu) | |
|---|---|---|---|---|---|---|
| 0 | 6981428.00000 | 6981580.00000 | 2023032.00000 | 3686160.00000 | 1272388.00000 | 0.00000 |
| 1 | 8354235.00000 | 8354396.00000 | 0.00000 | 3905411.00000 | 4448985.00000 | 0.00000 |
| 2 | 73130656.00000 | 73132739.00000 | 19660404.00000 | 49762435.00000 | 3709900.00000 | 0.00000 |
| 3 | 28229320.00000 | 28229435.00000 | 23458518.00000 | 2769023.00000 | 2001894.00000 | 0.00000 |
| 4 | 14829099.00000 | 14829350.00000 | 0.00000 | 6066245.00000 | 8763105.00000 | 0.00000 |
| 5 | 12051984.00000 | 12052287.00000 | 0.00000 | 7271004.00000 | 4781283.00000 | 0.00000 |
| 6 | 6252842.00000 | 6252953.00000 | 0.00000 | 2679698.00000 | 3573255.00000 | 0.00000 |
| 7 | 6426022.00000 | 6426191.00000 | 2003108.00000 | 4108004.00000 | 315079.00000 | 0.00000 |
| 8 | 12633744.00000 | 12634049.00000 | 0.00000 | 7355649.00000 | 5278400.00000 | 0.00000 |
| 9 | 4587231.00000 | 4587329.00000 | 1483476.00000 | 2370050.00000 | 733803.00000 | 0.00000 |
| 10 | 14719853.00000 | 14720117.00000 | 3623542.00000 | 6337531.00000 | 4759044.00000 | 0.00000 |
| 11 | 27717026.00000 | 27717818.00000 | 5426396.00000 | 19058722.00000 | 3232700.00000 | 0.00000 |
| 12 | 6807227.00000 | 6807294.00000 | 1987163.00000 | 1615057.00000 | 3205074.00000 | 0.00000 |
| 13 | 22811690.00000 | 22812276.00000 | 4731090.00000 | 14090906.00000 | 3990280.00000 | 0.00000 |
| 14 | 10711451.00000 | 10711620.00000 | 4403788.00000 | 4089407.00000 | 2218425.00000 | 0.00000 |
| 15 | 16849234.00000 | 16849902.00000 | 703184.00000 | 16146718.00000 | 0.00000 | 0.00000 |
| 16 | 10340282.00000 | 10340501.00000 | 4398022.00000 | 5288219.00000 | 654260.00000 | 0.00000 |
| 17 | 33270518.00000 | 33271430.00000 | 0.00000 | 21932264.00000 | 11339166.00000 | 0.00000 |
| 18 | 7481768.00000 | 7481923.00000 | 1755365.00000 | 3773975.00000 | 1952583.00000 | 0.00000 |
| 19 | 7845112.00000 | 7845186.00000 | 3205497.00000 | 1790665.00000 | 2849024.00000 | 0.00000 |
| 20 | 53332648.00000 | 53333762.00000 | 0.00000 | 26798906.00000 | 26534856.00000 | 0.00000 |
| 21 | 50899248.00000 | 50900065.00000 | 0.00000 | 19677268.00000 | 31222797.00000 | 0.00000 |
| 22 | 1454222.00000 | 1454259.00000 | 0.00000 | 911659.00000 | 542600.00000 | 0.00000 |
| 23 | 3880806.00000 | 3880900.00000 | 0.00000 | 2254751.00000 | 1626149.00000 | 0.00000 |
| 24 | 9791557.00000 | 9791747.00000 | 0.00000 | 4582663.00000 | 5209084.00000 | 0.00000 |
| 25 | 1388717.00000 | 1388775.00000 | 0.00000 | 1388775.00000 | 0.00000 | 0.00000 |
| 26 | 5159342.00000 | 5159364.00000 | 0.00000 | 532747.00000 | 4626617.00000 | 0.00000 |
| 27 | 11026945.00000 | 11027185.00000 | 0.00000 | 5763595.00000 | 5263590.00000 | 0.00000 |
| 28 | 6283226.00000 | 6283306.00000 | 0.00000 | 1933106.00000 | 4350200.00000 | 0.00000 |
| 29 | 4341706.00000 | 4341800.00000 | 0.00000 | 2276244.00000 | 2065556.00000 | 0.00000 |
La différence entre les colonnes 'SiteEnergyUse' et 'Total_EnergyUse'
est minime, mais elle existe cependant.
Pour cette raison, je choisis de calculer les proportions de chaque colonne
à partir de l'addition des 4 colonnes existantes c'est à dire via la colonne 'Total_EnergyUse'.
Je vérifie si la valeur absolue de 1 moins le rapport
entre '**Total_EnergyUse**' et '**SiteEnergyUse**'
reste minime sur l'ensemble du DataFrame :
sns.boxplot(np.abs(1-(df['Total_EnergyUse']/df['SiteEnergyUse'])))
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. warnings.warn(
<AxesSubplot:>
Certains enregistrements ont des valeurs différentes de presque 140% !
Affichage de la valeur absolue de 1 moins le rapport entre
'**Total_EnergyUse**' et '**SiteEnergyUse**' :
np.abs(1-(df['Total_EnergyUse']/df['SiteEnergyUse'])).sort_values(ascending=False).head(30)
6546 1.33676 5108 1.00000 6688 1.00000 5820 1.00000 3853 1.00000 5661 1.00000 4663 1.00000 3788 0.82567 6101 0.79023 5597 0.72845 6131 0.72012 3845 0.67080 6690 0.64503 4015 0.50969 3941 0.49159 6617 0.44077 4879 0.34258 1201 0.31423 5223 0.29674 5538 0.28741 1308 0.28497 6064 0.27350 6615 0.25626 6611 0.25047 5781 0.23735 3903 0.21438 4095 0.19026 3411 0.18797 4620 0.18651 5935 0.15359 dtype: float64
Pour plus de fiabilité, je filtre et retire les enregistrements qui ont un rapport de plus de 1%
entre la valeur absolue de 1 moins le rapport entre '**Total_EnergyUse**' et '**SiteEnergyUse**' :
print('Nombre d\'enregistrements avant traitement :',df.shape[0])
print('Nombre de lignes supprimées :',len(df[np.abs(1-(df['Total_EnergyUse']/df['SiteEnergyUse'])) >= 0.01]))
df = df[np.abs(1-(df['Total_EnergyUse']/df['SiteEnergyUse'])) < 0.01]
print('Nombre d\'enregistrements après traitement :',df.shape[0])
Nombre d'enregistrements avant traitement : 6681 Nombre de lignes supprimées : 45 Nombre d'enregistrements après traitement : 6636
Je crée les 4 colonnes contenant les proportions
d'énergie utilisées via la fonction *calcProportionEnergies* :
def calcProportionEnergies(df):
'''
Calcule la proportion de chaque colonne par rapport
au total des colonnes passées en argument.
Retourne le DataFrame passé en argument.'''
data = df.copy()
# Initialisation de la colonne 'Total' contenant le total des 4 énergies
data['TOTAL'] = 0
# Imputation des valeurs manquantes et renseignement de la colonne 'Total'
for col in ['Electricity(kBtu)', 'NaturalGas(kBtu)', 'SteamUse(kBtu)', 'OtherFuelUse(kBtu)']:
data[col] = data[col].fillna(0)
data['TOTAL'] += data[col]
for key, value in {'Proportion_Electricity':'Electricity(kBtu)',
'Proportion_NaturalGas':'NaturalGas(kBtu)',
'Proportion_Steam': 'SteamUse(kBtu)',
'Proportion_OtherFuel': 'OtherFuelUse(kBtu)'}.items():
data[key] = data[value] / data['TOTAL']
# Je ne retourne que 3 colonnes sur 4 car la dernière peut-être déduite des 3 premières.
return data.drop(['TOTAL','Proportion_OtherFuel'], axis=1)
df = calcProportionEnergies(df)
Je peux maintenant retirer la colonne '**Total_EnergyUse**' :
df.drop('Total_EnergyUse', axis=1, inplace=True)
Affichage du DataFrame '**df**' via ses valeurs manquantes :
plt.figure(figsize=(20,10))
sns.heatmap(df.isna(), cbar=False)
<AxesSubplot:>
La colonne 'BuildingType' nous indique la nature des bâtiments
et nous permet d'identifier les bâtiments résidentiels et non résidentiels.
df.BuildingType.value_counts()
NonResidential 2898 Multifamily LR (1-4) 2029 Multifamily MR (5-9) 1126 Multifamily HR (10+) 217 SPS-District K-12 177 Nonresidential COS 144 Campus 44 Nonresidential WA 1 Name: BuildingType, dtype: int64
Je ne conserve que les bâtiments **Non Résidentiels** :
df = df[df.BuildingType.isin(['NonResidential','SPS-District K-12','Nonresidential COS','Campus','Nonresidential WA'])]
L'objectif du projet est de tenter de prédire au mieux les données
de consommation énergétique et d'émission de CO2 sans l'aide
des relevés des agents de terrain.
Je supprime donc toutes les données
provenant d'un relevé réalisé par un agent :
colReleveEnergie = df.columns[df.columns.str.contains('\(k') | df.columns.str.contains('\(t')].tolist()+['GHGEmissionsIntensity']
print('Liste des colonnes supprimées :',colReleveEnergie)
df.drop(colReleveEnergie, axis=1, inplace=True)
print('Nombre de colonnes dans le DataFrame \'df\': ',df.shape[1])
Liste des colonnes supprimées : ['SiteEUI(kBtu/sf)', 'SiteEUIWN(kBtu/sf)', 'SourceEUI(kBtu/sf)', 'SourceEUIWN(kBtu/sf)', 'SiteEnergyUseWN(kBtu)', 'SteamUse(kBtu)', 'Electricity(kWh)', 'Electricity(kBtu)', 'NaturalGas(therms)', 'NaturalGas(kBtu)', 'OtherFuelUse(kBtu)', 'GHGEmissionsIntensity'] Nombre de colonnes dans le DataFrame 'df': 42
J'itère les modalités des colonnes et j'identifie les colonnes constantes :
J'analyse également les colonnes sur la base de leur description
contenue dans les fichiers JSON du projet.
for col in df.columns:
print(f'{col :-<75} {len(df[col].value_counts())}')
OSEBuildingID-------------------------------------------------------------- 1692 BuildingType--------------------------------------------------------------- 5 PrimaryPropertyType-------------------------------------------------------- 30 PropertyName--------------------------------------------------------------- 3152 TaxParcelIdentificationNumber---------------------------------------------- 1825 CouncilDistrictCode-------------------------------------------------------- 7 Neighborhood--------------------------------------------------------------- 19 NumberofBuildings---------------------------------------------------------- 17 NumberofFloors------------------------------------------------------------- 45 PropertyGFATotal----------------------------------------------------------- 1659 PropertyGFAParking--------------------------------------------------------- 365 PropertyGFABuilding-------------------------------------------------------- 1686 ListOfAllPropertyUseTypes-------------------------------------------------- 385 LargestPropertyUseType----------------------------------------------------- 57 LargestPropertyUseTypeGFA-------------------------------------------------- 1676 SecondLargestPropertyUseType----------------------------------------------- 47 SecondLargestPropertyUseTypeGFA-------------------------------------------- 749 ThirdLargestPropertyUseType------------------------------------------------ 40 ThirdLargestPropertyUseTypeGFA--------------------------------------------- 324 YearsENERGYSTARCertified--------------------------------------------------- 112 ENERGYSTARScore------------------------------------------------------------ 100 SiteEnergyUse-------------------------------------------------------------- 3263 TotalGHGEmissions---------------------------------------------------------- 2979 DefaultData---------------------------------------------------------------- 4 Comments------------------------------------------------------------------- 6 ComplianceStatus----------------------------------------------------------- 3 Outlier-------------------------------------------------------------------- 4 2010 Census Tracts--------------------------------------------------------- 12 Seattle Police Department Micro Community Policing Plan Areas-------------- 61 City Council Districts----------------------------------------------------- 2 SPD Beats------------------------------------------------------------------ 51 Zip Codes------------------------------------------------------------------ 29 Address-------------------------------------------------------------------- 3067 City----------------------------------------------------------------------- 2 State---------------------------------------------------------------------- 1 ZipCode-------------------------------------------------------------------- 47 Latitude------------------------------------------------------------------- 3033 Longitude------------------------------------------------------------------ 2931 ageBuilding---------------------------------------------------------------- 115 Proportion_Electricity----------------------------------------------------- 2447 Proportion_NaturalGas------------------------------------------------------ 2346 Proportion_Steam----------------------------------------------------------- 220
J'affiche également les différentes valeurs des variables *Object* pour identifier
des variables qui auront besoin d'être formatées suite au merge des deux DataFrames
correspondant aux années 2015 et 2016 :
print('='*70)
for col in df.select_dtypes('object'):
print(f'{col :-<50} {df[col].unique()}')
print('='*70)
====================================================================== BuildingType-------------------------------------- ['NonResidential' 'Nonresidential COS' 'SPS-District K-12' 'Campus' 'Nonresidential WA'] ====================================================================== PrimaryPropertyType------------------------------- ['Hotel' 'Other' 'Mixed Use Property' 'K-12 School' 'College/University' 'Small- and Mid-Sized Office' 'Self-Storage Facility\n' 'Distribution Center' 'Large Office' 'Senior Care Community' 'Medical Office' 'Retail Store' 'Hospital' 'Residence Hall/Dormitory' 'Non-Refrigerated Warehouse' 'Distribution Center\n' 'SPS-District K-12' 'Worship Facility' 'Supermarket/Grocery Store' 'Laboratory' 'Self-Storage Facility' 'Refrigerated Warehouse' 'Restaurant\n' 'Low-Rise Multifamily' 'Restaurant' 'University' 'Warehouse' 'Residence Hall' 'Supermarket / Grocery Store' 'Office'] ====================================================================== PropertyName-------------------------------------- ['MAYFLOWER PARK HOTEL' 'PARAMOUNT HOTEL' 'WESTIN HOTEL' ... 'Queen Anne Pool' 'South Park Community Center' 'Van Asselt Community Center'] ====================================================================== TaxParcelIdentificationNumber--------------------- ['659000030' '659000220' '659000475' ... '3558300000' '7883603155' '7857002030'] ====================================================================== Neighborhood-------------------------------------- ['DOWNTOWN' 'SOUTHEAST' 'NORTHEAST' 'EAST' 'CENTRAL' 'NORTH' 'MAGNOLIA / QUEEN ANNE' 'LAKE UNION' 'GREATER DUWAMISH' 'BALLARD' 'NORTHWEST' 'SOUTHWEST' 'DELRIDGE' 'Ballard' 'North' 'Delridge' 'Northwest' 'Central' 'DELRIDGE NEIGHBORHOODS'] ====================================================================== ListOfAllPropertyUseTypes------------------------- ['Hotel' 'Hotel, Parking, Restaurant' 'Hotel, Parking, Swimming Pool' 'Police Station' 'Other - Entertainment/Public Assembly' nan 'Hotel, Parking' 'Data Center, Library, Parking' 'Fitness Center/Health Club/Gym, Office, Swimming Pool' 'Office, Other, Other - Lodging/Residential, Restaurant, Social/Meeting Hall' 'Courthouse' 'Other' 'K-12 School' 'College/University' 'Automobile Dealership' 'Hotel, Parking, Retail Store, Swimming Pool' 'K-12 School, Multifamily Housing, Office, Other - Entertainment/Public Assembly' 'Self-Storage Facility' 'Non-Refrigerated Warehouse, Office' 'Office, Other - Education' 'Office, Self-Storage Facility, Vocational School' 'Data Center, Office, Retail Store' 'Office, Retail Store' 'Office, Other - Entertainment/Public Assembly, Restaurant' 'Hotel, Parking, Restaurant, Retail Store' 'Hotel, Other - Entertainment/Public Assembly, Retail Store' 'Hotel, Restaurant' 'Office' 'Parking, Senior Care Community' 'Senior Care Community' 'Medical Office' 'Data Center, Medical Office, Non-Refrigerated Warehouse, Office, Other, Parking' 'Retail Store' 'Office, Other, Parking, Retail Store' 'Office, Other, Parking' 'Data Center, Distribution Center, Office, Parking, Retail Store' 'Hotel, Swimming Pool' 'Office, Parking' 'Other, Parking' 'Hospital (General Medical & Surgical), Parking' 'Office, Other' 'Museum' 'Data Center, Office' 'Repair Services (Vehicle, Shoe, Locksmith, etc)' 'Food Service, Office, Other - Lodging/Residential' 'Residence Hall/Dormitory' 'K-12 School, Parking' 'Courthouse, Other' 'Non-Refrigerated Warehouse, Office, Other - Services' 'Restaurant, Retail Store' 'Other/Specialty Hospital' 'Hospital (General Medical & Surgical)' 'Financial Office, Medical Office, Parking' 'Non-Refrigerated Warehouse' 'Non-Refrigerated Warehouse, Office, Other - Services, Residence Hall/Dormitory' 'Data Center, Distribution Center, Office, Other, Parking' 'Office, Restaurant, Self-Storage Facility' 'Other - Entertainment/Public Assembly, Parking' 'Data Center, Distribution Center, Office, Other' 'Non-Refrigerated Warehouse, Office, Other, Other - Services' 'Food Service, Hotel, Parking' 'Laboratory, Office, Parking' 'Fast Food Restaurant, Medical Office, Movie Theater, Parking, Retail Store' 'Financial Office, Multifamily Housing, Parking, Retail Store, Supermarket/Grocery Store' 'Hotel, Other, Parking, Restaurant, Retail Store' 'Non-Refrigerated Warehouse, Parking' 'K-12 School, Medical Office, Office, Restaurant, Retail Store, Senior Care Community' 'Office, Restaurant' 'Worship Facility' 'Office, Other, Other - Entertainment/Public Assembly' 'Office, Residence Hall/Dormitory, Retail Store' 'Medical Office, Office' 'Other, Restaurant, Retail Store' 'Non-Refrigerated Warehouse, Other - Services' 'Financial Office, Laboratory, Office, Parking' 'Office, Other, Parking, Restaurant' 'Office, Parking, Restaurant' 'Office, Other - Recreation, Parking, Retail Store' 'Fitness Center/Health Club/Gym, Office, Other, Parking, Restaurant' 'Office, Retail Store, Social/Meeting Hall' 'Convenience Store without Gas Station, Food Service, Office' 'Office, Parking, Pre-school/Daycare' 'Convenience Store without Gas Station, Office, Parking' 'Data Center, Office, Parking' 'Financial Office, Food Sales, Office, Other, Parking' 'Convenience Store without Gas Station, Data Center, Financial Office, Fitness Center/Health Club/Gym, Office, Other, Parking, Personal Services (Health/Beauty, Dry Cleaning, etc), Restaurant' 'Non-Refrigerated Warehouse, Office, Retail Store' 'Data Center, Office, Other' 'Fitness Center/Health Club/Gym, Office' 'Laboratory, Office, Other, Parking' 'Supermarket/Grocery Store' 'Office, Other, Restaurant' 'Office, Other - Restaurant/Bar, Retail Store, Single Family Home' 'Non-Refrigerated Warehouse, Other' 'Office, Performing Arts, Retail Store' 'Office, Other - Restaurant/Bar, Parking' 'Office, Other - Education, Parking' 'Financial Office, Office, Other, Parking, Restaurant' 'Hotel, Office, Retail Store' 'Distribution Center, Non-Refrigerated Warehouse, Office' 'College/University, Office, Restaurant' 'Financial Office, Office, Other, Parking, Restaurant, Retail Store' 'Office, Other, Retail Store' 'Office, Parking, Retail Store' 'Office, Restaurant, Retail Store' 'Bank Branch, Office, Other, Parking, Restaurant' 'Medical Office, Office, Supermarket/Grocery Store' 'Food Sales, Multifamily Housing, Office, Other - Education, Other - Restaurant/Bar, Pre-school/Daycare, Restaurant, Retail Store, Supermarket/Grocery Store' 'Office, Other - Restaurant/Bar, Parking, Retail Store' 'Movie Theater, Other - Restaurant/Bar, Parking' 'Laboratory, Office' 'Data Center, Office, Parking, Restaurant, Retail Store' 'Other, Retail Store' 'Convention Center, Parking' 'Urgent Care/Clinic/Other Outpatient' 'Medical Office, Parking' 'Data Center, Financial Office, Office, Other, Parking, Restaurant' 'Data Center, Office, Other, Parking, Restaurant' 'Distribution Center, Office' 'Distribution Center' 'Adult Education, Bank Branch, Office, Parking' 'Data Center, Office, Other, Restaurant' 'Parking, Retail Store, Supermarket/Grocery Store' 'Data Center, Laboratory' 'Laboratory' 'Financial Office, Food Service, Office, Other, Parking' 'Medical Office, Office, Parking' 'Fitness Center/Health Club/Gym, Office, Parking, Restaurant' 'Bank Branch, Office, Parking' 'Non-Refrigerated Warehouse, Office, Other, Other - Entertainment/Public Assembly, Parking' 'Office, Other - Services' 'Parking, Strip Mall' 'Wholesale Club/Supercenter' 'Retail Store, Supermarket/Grocery Store' 'Parking, Supermarket/Grocery Store' 'Data Center, Library, Non-Refrigerated Warehouse, Office, Other - Entertainment/Public Assembly, Other - Recreation, Other - Services, Restaurant, Social/Meeting Hall' 'Multifamily Housing, Non-Refrigerated Warehouse, Office, Parking, Retail Store' 'Parking, Retail Store' 'Parking, Self-Storage Facility' 'Distribution Center, Parking' 'Office, Parking, Refrigerated Warehouse' 'Office, Other - Services, Parking' 'Strip Mall' 'Medical Office, Retail Store' 'Refrigerated Warehouse' 'Multifamily Housing, Parking, Restaurant, Retail Store' 'Food Service, Office, Parking, Restaurant' 'Data Center, Office, Other, Other - Entertainment/Public Assembly' 'Non-Refrigerated Warehouse, Retail Store' 'Distribution Center, Office, Other' 'Multifamily Housing, Non-Refrigerated Warehouse, Office, Parking' 'Distribution Center, Office, Parking' 'Distribution Center, Non-Refrigerated Warehouse, Office, Retail Store' 'Distribution Center, Office, Other, Other - Recreation, Parking' 'Medical Office, Other, Parking' 'Financial Office, Hotel, Multifamily Housing, Other, Parking, Restaurant, Supermarket/Grocery Store' 'Data Center, Office, Restaurant' 'Data Center, Financial Office, Office, Parking, Restaurant' 'Other - Recreation, Parking, Swimming Pool' 'Lifestyle Center' 'Data Center, Office, Parking, Restaurant' 'Non-Refrigerated Warehouse, Office, Other, Other - Recreation, Other - Services, Restaurant' 'Adult Education, Fitness Center/Health Club/Gym, Office, Other - Restaurant/Bar, Parking, Retail Store' 'Non-Refrigerated Warehouse, Office, Other' 'Distribution Center, Office, Retail Store' 'Other - Public Services, Parking' 'Multifamily Housing, Non-Refrigerated Warehouse, Office, Parking, Retail Store, Worship Facility' 'Fitness Center/Health Club/Gym, Office, Parking' 'Data Center, Office, Other, Parking' 'Bar/Nightclub, Office, Personal Services (Health/Beauty, Dry Cleaning, etc)' 'Medical Office, Parking, Retail Store' 'Office, Other - Entertainment/Public Assembly, Other - Mall, Restaurant' 'Office, Performing Arts, Restaurant, Retail Store, Self-Storage Facility' 'Office, Other, Other - Restaurant/Bar, Parking' 'Financial Office, Food Service, Office, Other' 'Data Center, Medical Office, Office, Parking, Restaurant' 'College/University, Parking' 'Data Center, Non-Refrigerated Warehouse, Office, Retail Store' 'Non-Refrigerated Warehouse, Other, Parking, Retail Store' 'Other, Parking, Restaurant, Retail Store' 'Distribution Center, Non-Refrigerated Warehouse, Refrigerated Warehouse' 'Non-Refrigerated Warehouse, Office, Other - Services, Retail Store' 'Multifamily Housing, Office, Parking, Retail Store' 'Non-Refrigerated Warehouse, Other, Restaurant, Retail Store' 'Non-Refrigerated Warehouse, Office, Other - Recreation' 'Medical Office, Office, Other' 'Other - Entertainment/Public Assembly, Restaurant' 'Data Center, Non-Refrigerated Warehouse, Other, Refrigerated Warehouse, Repair Services (Vehicle, Shoe, Locksmith, etc), Restaurant' 'Automobile Dealership, Office' 'Manufacturing/Industrial Plant, Office, Parking' 'Manufacturing/Industrial Plant' 'Office, Other, Retail Store, Swimming Pool' 'Medical Office, Office, Other, Other - Services, Parking, Retail Store' 'Parking, Restaurant' 'Social/Meeting Hall' 'Restaurant' 'Office, Other - Lodging/Residential' 'Bank Branch, Office' 'Other - Education' 'K-12 School, Worship Facility' 'Bank Branch, Other, Parking' 'Financial Office' 'Fire Station' 'Other - Entertainment/Public Assembly, Parking, Social/Meeting Hall, Swimming Pool' 'Non-Refrigerated Warehouse, Office, Other, Retail Store' 'Non-Refrigerated Warehouse, Other, Retail Store' 'Bank Branch, Financial Office' 'Bank Branch, Senior Care Community' 'Parking, Worship Facility' 'Medical Office, Multifamily Housing, Office, Restaurant, Retail Store' 'Multifamily Housing, Other - Entertainment/Public Assembly' 'Other, Worship Facility' 'Parking, Residence Hall/Dormitory' 'Office, Residence Hall/Dormitory' 'Office, Social/Meeting Hall' 'Performing Arts' 'Non-Refrigerated Warehouse, Office, Other - Recreation, Restaurant' 'Other - Recreation, Parking, Restaurant' 'Fitness Center/Health Club/Gym, Swimming Pool' 'Food Service, Other - Services, Retail Store, Supermarket/Grocery Store' 'Other - Mall, Restaurant' 'Distribution Center, Office, Refrigerated Warehouse' 'K-12 School, Office, Retail Store' 'Other - Recreation' 'Other - Services' 'Fitness Center/Health Club/Gym, Office, Other - Recreation, Pre-school/Daycare' 'Non-Refrigerated Warehouse, Office, Self-Storage Facility' 'Automobile Dealership, Parking' 'Non-Refrigerated Warehouse, Other - Recreation' 'Multifamily Housing, Office, Other - Mall, Parking, Restaurant' 'Hotel, Retail Store' 'Bank Branch, Bar/Nightclub, Office, Parking' 'Multifamily Housing, Office' 'Residential Care Facility' 'Bank Branch' 'Other - Education, Parking' 'Food Service, Office, Other - Entertainment/Public Assembly' 'Office, Other, Other - Services, Residence Hall/Dormitory, Restaurant' 'Food Service, Office, Parking' 'Bank Branch, Office, Parking, Restaurant' 'Other - Entertainment/Public Assembly, Restaurant, Retail Store' 'Other, Personal Services (Health/Beauty, Dry Cleaning, etc), Restaurant, Retail Store' 'Bar/Nightclub, Food Sales, Multifamily Housing, Restaurant, Retail Store, Self-Storage Facility' 'Food Sales, Office, Restaurant' 'Food Sales, Manufacturing/Industrial Plant, Office, Other - Entertainment/Public Assembly, Other - Restaurant/Bar, Restaurant, Retail Store, Self-Storage Facility' 'Multifamily Housing, Office, Restaurant' 'Food Service' 'Hotel, Multifamily Housing, Parking, Retail Store' 'Medical Office, Office, Retail Store' 'Other - Recreation, Self-Storage Facility' 'Office, Repair Services (Vehicle, Shoe, Locksmith, etc), Retail Store' 'Adult Education, Parking' 'Medical Office, Non-Refrigerated Warehouse' 'Office, Worship Facility' 'Office, Other - Recreation' 'Medical Office, Non-Refrigerated Warehouse, Office, Retail Store' 'Bank Branch, Medical Office, Parking, Retail Store' 'Library, Other - Services' 'Distribution Center, Non-Refrigerated Warehouse' 'Multifamily Housing, Office, Other, Strip Mall' 'Fitness Center/Health Club/Gym, Other - Recreation, Swimming Pool' 'Office, Parking, Self-Storage Facility' 'Office, Repair Services (Vehicle, Shoe, Locksmith, etc)' 'Medical Office, Office, Worship Facility' 'Medical Office, Other/Specialty Hospital, Outpatient Rehabilitation/Physical Therapy, Parking' 'Adult Education' 'Museum, Parking' 'Bank Branch, Multifamily Housing, Office, Other, Retail Store' 'Office, Other - Utility, Parking' 'Office, Restaurant, Supermarket/Grocery Store' 'Multifamily Housing, Non-Refrigerated Warehouse, Office, Retail Store' 'Other - Recreation, Worship Facility' 'Non-Refrigerated Warehouse, Office, Other, Parking, Retail Store' 'Medical Office, Non-Refrigerated Warehouse, Office, Other' 'Other - Lodging/Residential, Parking' 'Manufacturing/Industrial Plant, Parking' 'Office, Refrigerated Warehouse' 'Office, Other - Public Services' 'Non-Refrigerated Warehouse, Parking, Repair Services (Vehicle, Shoe, Locksmith, etc), Retail Store' 'Food Service, Non-Refrigerated Warehouse, Retail Store' 'Fitness Center/Health Club/Gym, Office, Other - Recreation' 'Distribution Center, Non-Refrigerated Warehouse, Office, Other - Education, Parking' 'Movie Theater' 'Fast Food Restaurant, Other, Other - Entertainment/Public Assembly' 'College/University, Medical Office' 'Fitness Center/Health Club/Gym, Office, Other - Recreation, Other - Technology/Science, Pre-school/Daycare, Swimming Pool' 'Food Service, Other, Other - Education, Other - Entertainment/Public Assembly, Other - Recreation, Social/Meeting Hall' 'Other, Other - Entertainment/Public Assembly, Self-Storage Facility' 'Other, Refrigerated Warehouse' 'Non-Refrigerated Warehouse, Office, Social/Meeting Hall' 'Non-Refrigerated Warehouse, Other, Refrigerated Warehouse' 'Other - Mall, Parking' 'Non-Refrigerated Warehouse, Worship Facility' 'Outpatient Rehabilitation/Physical Therapy, Parking' 'Personal Services (Health/Beauty, Dry Cleaning, etc)' 'Non-Refrigerated Warehouse, Office, Repair Services (Vehicle, Shoe, Locksmith, etc)' 'Hotel, Multifamily Housing, Parking' 'College/University, Restaurant' 'Office, Other, Refrigerated Warehouse' 'Other, Parking, Restaurant' 'Multifamily Housing, Office, Other, Parking' 'Financial Office, Multifamily Housing, Parking, Residence Hall/Dormitory' 'Office, Restaurant, Retail Store, Self-Storage Facility' 'Bar/Nightclub, Multifamily Housing' 'Office, Other, Worship Facility' 'Food Sales, Non-Refrigerated Warehouse, Office' 'Multifamily Housing, Office, Other - Entertainment/Public Assembly, Parking' 'Medical Office, Office, Other, Restaurant, Retail Store' 'Parking, Senior Care Community, Worship Facility' 'Multifamily Housing, Office, Parking, Restaurant' 'Office, Parking, Residence Hall/Dormitory, Restaurant, Social/Meeting Hall' 'Office, Other, Other - Entertainment/Public Assembly, Other - Recreation, Other - Utility, Restaurant, Retail Store' 'Bank Branch, Parking' 'Office, Other - Restaurant/Bar' 'Manufacturing/Industrial Plant, Office, Retail Store' 'Food Service, Library, Office, Parking' 'K-12 School, Office, Supermarket/Grocery Store, Worship Facility' 'Bar/Nightclub, Food Sales, Museum, Retail Store, Supermarket/Grocery Store' 'Office, Other - Entertainment/Public Assembly' 'Other - Lodging/Residential' 'Multifamily Housing, Retail Store, Social/Meeting Hall' 'Multifamily Housing, Office, Other' 'Fitness Center/Health Club/Gym, Office, Other - Recreation, Other - Technology/Science, Parking, Pre-school/Daycare' 'Other - Education, Performing Arts, Social/Meeting Hall' 'Office, Supermarket/Grocery Store' 'Fitness Center/Health Club/Gym, Medical Office, Office, Parking, Restaurant' 'Non-Refrigerated Warehouse, Office, Parking' 'Non-Refrigerated Warehouse, Office, Restaurant, Retail Store' 'Office, Other - Entertainment/Public Assembly, Restaurant, Urgent Care/Clinic/Other Outpatient' 'Non-Refrigerated Warehouse, Office, Refrigerated Warehouse' 'Other - Education, Worship Facility' 'Library, Parking' 'Multifamily Housing, Parking, Supermarket/Grocery Store' 'Other - Services, Retail Store' 'K-12 School, Office, Social/Meeting Hall, Worship Facility' 'Multifamily Housing, Parking, Retail Store' 'Laboratory, Other' 'Distribution Center, Non-Refrigerated Warehouse, Supermarket/Grocery Store' 'Enclosed Mall, Manufacturing/Industrial Plant, Office, Other - Restaurant/Bar' 'Multifamily Housing, Office, Parking' 'Other - Utility' 'Hospital (General Medical & Surgical), Other' 'Hotel, Parking, Retail Store' 'Data Center, Multifamily Housing, Office, Other, Parking' 'Data Center, Parking' 'Convenience Store without Gas Station, Laboratory, Parking' 'Lifestyle Center, Medical Office' 'Parking, Pre-school/Daycare' 'Fitness Center/Health Club/Gym, Office, Other - Education, Other - Recreation, Other - Technology/Science, Pre-school/Daycare, Swimming Pool' 'Office, Other - Public Services, Personal Services (Health/Beauty, Dry Cleaning, etc), Senior Care Community' 'Pre-school/Daycare' 'Energy/Power Station, Laboratory, Manufacturing/Industrial Plant, Non-Refrigerated Warehouse, Office' 'Other - Mall, Personal Services (Health/Beauty, Dry Cleaning, etc)' 'Laboratory, Non-Refrigerated Warehouse, Office, Other - Utility' 'Financial Office, Office, Parking, Retail Store' 'Data Center' 'Office, Parking, Personal Services (Health/Beauty, Dry Cleaning, etc)' 'Data Center, Office, Parking, Performing Arts, Restaurant, Retail Store' 'Fitness Center/Health Club/Gym, Office, Other - Education, Other - Recreation, Other - Restaurant/Bar, Outpatient Rehabilitation/Physical Therapy, Personal Services (Health/Beauty, Dry Cleaning, etc), Restaurant, Retail Store, Self-Storage Facility, Vocat' 'Enclosed Mall, Office' 'Office, Other - Restaurant/Bar, Other - Technology/Science, Parking' 'Office, Other - Technology/Science, Parking' 'Hotel, Parking, Restaurant, Swimming Pool' 'Laboratory, Medical Office' 'Manufacturing/Industrial Plant, Office, Parking, Retail Store' 'Multifamily Housing' 'Bank Branch, Parking, Senior Care Community' 'Food Service, Retail Store, Supermarket/Grocery Store' 'Food Sales, Office, Restaurant, Retail Store, Self-Storage Facility, Social/Meeting Hall' 'Data Center, Non-Refrigerated Warehouse, Office' 'Other, Supermarket/Grocery Store' 'Bank Branch, Retail Store' 'Pre-school/Daycare, Worship Facility' 'Fitness Center/Health Club/Gym, Office, Other, Other - Utility, Restaurant, Retail Store' 'Office, Other - Entertainment/Public Assembly, Other - Public Services, Restaurant, Urgent Care/Clinic/Other Outpatient' 'Office, Other, Other - Restaurant/Bar, Strip Mall' 'Multifamily Housing, Parking, Personal Services (Health/Beauty, Dry Cleaning, etc), Retail Store, Supermarket/Grocery Store' 'Residence Hall/Dormitory, Supermarket/Grocery Store' 'Movie Theater, Office, Vocational School' 'Multifamily Housing, Office, Parking, Restaurant, Retail Store' 'Data Center, Laboratory, Museum, Office, Other, Parking' 'Fitness Center/Health Club/Gym, Office, Other - Recreation, Pre-school/Daycare, Social/Meeting Hall' 'Prison/Incarceration' 'Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/Daycare' 'Fitness Center/Health Club/Gym, Office, Other - Recreation, Swimming Pool' 'Fitness Center/Health Club/Gym, Office, Other - Recreation, Other - Technology/Science'] ====================================================================== LargestPropertyUseType---------------------------- ['Hotel' 'Police Station' 'Other - Entertainment/Public Assembly' nan 'Library' 'Fitness Center/Health Club/Gym' 'Social/Meeting Hall' 'Courthouse' 'Other' 'K-12 School' 'College/University' 'Automobile Dealership' 'Office' 'Self-Storage Facility' 'Senior Care Community' 'Medical Office' 'Retail Store' 'Hospital (General Medical & Surgical)' 'Museum' 'Repair Services (Vehicle, Shoe, Locksmith, etc)' 'Other - Lodging/Residential' 'Residence Hall/Dormitory' 'Other/Specialty Hospital' 'Financial Office' 'Non-Refrigerated Warehouse' 'Distribution Center' 'Parking' 'Multifamily Housing' 'Worship Facility' 'Laboratory' 'Supermarket/Grocery Store' 'Convention Center' 'Urgent Care/Clinic/Other Outpatient' 'Other - Services' 'Strip Mall' 'Wholesale Club/Supercenter' 'Refrigerated Warehouse' 'Other - Recreation' 'Lifestyle Center' 'Other - Public Services' 'Data Center' 'Other - Mall' 'Manufacturing/Industrial Plant' 'Restaurant' 'Other - Education' 'Fire Station' 'Performing Arts' 'Residential Care Facility' 'Bank Branch' 'Other - Restaurant/Bar' 'Food Service' 'Adult Education' 'Other - Utility' 'Movie Theater' 'Outpatient Rehabilitation/Physical Therapy' 'Personal Services (Health/Beauty, Dry Cleaning, etc)' 'Pre-school/Daycare' 'Prison/Incarceration'] ====================================================================== SecondLargestPropertyUseType---------------------- [nan 'Parking' 'Office' 'Restaurant' 'K-12 School' 'Non-Refrigerated Warehouse' 'Other - Education' 'Vocational School' 'Retail Store' 'Other - Entertainment/Public Assembly' 'Data Center' 'Swimming Pool' 'Other' 'Courthouse' 'Residence Hall/Dormitory' 'Distribution Center' 'Self-Storage Facility' 'Other - Services' 'Laboratory' 'Movie Theater' 'Supermarket/Grocery Store' 'Medical Office' 'Other - Recreation' 'Convenience Store without Gas Station' 'Fitness Center/Health Club/Gym' 'Performing Arts' 'College/University' 'Other - Restaurant/Bar' 'Adult Education' 'Multifamily Housing' 'Bar/Nightclub' 'Financial Office' 'Repair Services (Vehicle, Shoe, Locksmith, etc)' 'Other - Lodging/Residential' 'Bank Branch' 'Refrigerated Warehouse' 'Hotel' 'Food Sales' 'Other - Public Services' 'Food Service' 'Worship Facility' 'Manufacturing/Industrial Plant' 'Social/Meeting Hall' 'Hospital (General Medical & Surgical)' 'Automobile Dealership' 'Personal Services (Health/Beauty, Dry Cleaning, etc)' 'Enclosed Mall' 'Pre-school/Daycare'] ====================================================================== ThirdLargestPropertyUseType----------------------- [nan 'Restaurant' 'Swimming Pool' 'Data Center' 'Office' 'Retail Store' 'Other - Entertainment/Public Assembly' 'Other' 'Distribution Center' 'Food Service' 'Other - Services' 'Medical Office' 'Parking' 'K-12 School' 'Fitness Center/Health Club/Gym' 'Social/Meeting Hall' 'Pre-school/Daycare' 'Convenience Store without Gas Station' 'Financial Office' 'Non-Refrigerated Warehouse' 'Other - Restaurant/Bar' 'Other - Education' 'Multifamily Housing' 'Laboratory' 'Bank Branch' 'Hotel' 'Other - Recreation' 'Personal Services (Health/Beauty, Dry Cleaning, etc)' 'Self-Storage Facility' 'Refrigerated Warehouse' 'Manufacturing/Industrial Plant' 'Bar/Nightclub' 'Other/Specialty Hospital' 'Fast Food Restaurant' 'Worship Facility' 'Supermarket/Grocery Store' 'Enclosed Mall' 'Other - Utility' 'Other - Technology/Science' 'Strip Mall' 'Vocational School'] ====================================================================== YearsENERGYSTARCertified-------------------------- [nan '2016' '2014' '2012' '2015' '2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008' '2010, 2009, 2007' '2010, 2007' '2016, 2015' '2017, 2015' '2015, 2014, 2012, 2011, 2009' '2012, 2009, 2008' '2016, 2015, 2014, 2013, 2011, 2010, 2009, 2007, 2000' '2016, 2009, 2008' '2016, 2014, 2012' '2016, 2015, 2014, 2012, 2008' '2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004' '2014, 2013, 2011, 2010, 2009, 2008, 2007' '2016, 2015, 2014, 2013, 2011, 2008' '2016, 2015, 2014, 2013, 2008' '2016, 2015, 2013, 2012, 2010' '2015, 2013, 2012, 2010' '2007' '2010' '2016, 2015, 2009, 2008' '2013, 2009, 2008' '2017' '2017, 2011, 2010, 2007' '2017, 2016, 2014, 2013, 2012, 2011, 2010, 2009, 2007' '2015, 2014, 2012, 2010, 2008' '2012, 2010, 2009, 2008' '2009, 2005' '2017, 2015, 2014, 2013' '2016, 2015, 2012' '2012, 2010, 2007, 2006' '2011, 2009' '2009' '2016, 2015, 2014, 2013, 2009, 2008' '2016, 2015, 2014, 2013, 2011, 2009' '2017, 2016, 2012' '2010, 2008' '2016, 2014, 2012, 2011, 2008, 2007' '2011' '2015, 2014, 2013, 2011, 2009, 2008' '2016, 2015, 2014, 2013, 2012, 2010, 2009' '2012, 2010' '2016, 2011, 2010, 2009' '2016, 2015, 2012, 2009' '2016, 2012' '2016, 2013, 2012, 2011, 2010, 2009, 2008' '2016, 2015, 2013, 2012, 2011, 2010, 2009, 2008' '2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2003, 2002, 2000' '2016, 2015, 2012, 2009, 2007, 2006' '2017, 2015, 2014, 2013, 2011' '2013' '2015, 2012' '2016, 2015, 2014' '2016, 2015, 2014, 2013, 2012, 2010, 2009, 2008' '2016, 2015, 2010, 2009, 2008' '2017201620152014201320122011201020092008' '201020092007' '201720102007' '20162015' '20172015' '20152014201220112009' '201220092008' '201620152014201320112010200920072000' '201620092008' '201620142012' '20162015201420122008' '2016201520142013201220112010200920082007200620052004' '201520142013' '2014201320112010200920082007' '2016201520142013' '2017201620152014201320112008' '201720162015201420132008' '20162015201320122010' '2015201320122010' '20162011201020092008' '20172016201520092008' '2017201320092008' '2017201120102007' '201720162014201320122011201020092007' '201720152014201220102008' '20092005' '2017201520142013' '201620152012' '2012201020072006' '20112009' '201620152014201320092008' '201620152014201320112009' '2016201520142013201220112010' '201720162012' '20102008' '201620142012201120082007' '2017201520142013201120092008' '2016201520142013201220102009' '2016201120102009' '2016201520122009' '20162012' '2016201320122011201020092008' '2017201620152012200920072006' '20162015201320122011201020092008' '2014201320122008' '201620152014201320122011201020092008200720062005200320022000' '2014201020082001' '20172015201420132011' '20172016' '20152012' '201720162015' '201620152014' '20162015201420132012201020092008' '20162015201020092008'] ====================================================================== DefaultData--------------------------------------- ['No' 'Yes' False True] ====================================================================== Comments------------------------------------------ [nan "Part of McKinstry's campus, property operates a fabrication and production shop and is conditioned by natural gas overhead radiant unit heaters with no cooling. Learn more at www.mckinstry.com." "One of two office buildings on McKinstry's campus partially remodeled in 2009 from warehouse to office space. Served by rooftop air handling units with underfloor VAVs. Self-performed energy efficiency upgrades in 2015 include a long-term LED lighting re" 'Part of McKinstry’s campus, this remodeled warehouse is mainly office but includes a full-size basketball court and gym with locker rooms, bistro with a full kitchen, and data center. Served by rooftop package units for heating and cooling. Upgrades inclu' 'Under construction starting 6/2015 (old building demolished) and ending 9/2017. New Building re-opens 9/2017. Year built changed from 1954 to 2017.' "Part of McKinstry's campus, the office space for this building sites on the 3rd floor above a 2-story parking garage and is served by rooftop package units for both heating and cooling. Learn more at www.mckinstryinnovationcenter.com." "Part of McKinstry's campus, property includes a warehouse, a local pipe supplier, and a local insulation contractor. Learn more at www.mckinstry.com."] ====================================================================== ComplianceStatus---------------------------------- ['Compliant' 'Error - Correct Default Data' 'Non-Compliant'] ====================================================================== Outlier------------------------------------------- [nan 'High Outlier' 'Low Outlier' 'High outlier' 'Low outlier'] ====================================================================== Address------------------------------------------- ['405 OLIVE WAY' '724 PINE ST' '1900 5TH AVE' ... '1920 1st Ave W' '8319 8th Ave S' '2820 S Myrtle St'] ====================================================================== City---------------------------------------------- ['SEATTLE' 'Seattle'] ====================================================================== State--------------------------------------------- ['WA'] ======================================================================
J'identifie les colonnes suivantes comme étant inutiles :
df.drop(['City',
'State',
'PropertyName',
'TaxParcelIdentificationNumber',
'Seattle Police Department Micro Community Policing Plan Areas',
'SPD Beats',
'Zip Codes',
'NumberofBuildings',
'Comments'], axis=1, inplace=True)
print('Nombre de colonnes dans le DataFrame \'df\': ',df.shape[1])
Nombre de colonnes dans le DataFrame 'df': 33
df[(df.TotalGHGEmissions <= 0) | (df.SiteEnergyUse <= 0)]
| OSEBuildingID | BuildingType | PrimaryPropertyType | CouncilDistrictCode | Neighborhood | NumberofFloors | PropertyGFATotal | PropertyGFAParking | PropertyGFABuilding | ListOfAllPropertyUseTypes | LargestPropertyUseType | LargestPropertyUseTypeGFA | SecondLargestPropertyUseType | SecondLargestPropertyUseTypeGFA | ThirdLargestPropertyUseType | ThirdLargestPropertyUseTypeGFA | YearsENERGYSTARCertified | ENERGYSTARScore | SiteEnergyUse | TotalGHGEmissions | DefaultData | ComplianceStatus | Outlier | 2010 Census Tracts | City Council Districts | Address | ZipCode | Latitude | Longitude | ageBuilding | Proportion_Electricity | Proportion_NaturalGas | Proportion_Steam |
|---|
Il n'existe pas d'enregistrement pour lesquelles les targets ont une valeur négative ou égale à 0.
Suite au listing des valeurs des colonnes Object réalisé précédement,
j'ai identifié des colonnes à formater (les valeurs de 2015 doivent
avoir le même format, orthographe ou casse que les valeurs de 2016).
df.DefaultData.value_counts()
False 1539 No 1450 Yes 188 True 87 Name: DefaultData, dtype: int64
df.DefaultData = df.DefaultData.map({True: True, False: False, 'Yes':True, 'No': False})
df.DefaultData.value_counts()
False 2989 True 275 Name: DefaultData, dtype: int64
La colonne **DefaultData** ne possède pas de valeur Null, je peux la convertir en colonne de type *Booleen* :
df.DefaultData = df.DefaultData.astype(bool)
def normAddress(address):
'''Cette fonction normalise l'écriture des adresses contenues dans la String <address> passées en argument.
On modifie le champs 'address' dans cet ordre avant de retourner la String ainsi modifiée :
1) Passage de la chaine en MAJUSCULE
2) Suppression des points '.'
3) a) 'STREET' remplacé par 'ST'
b) 'AVENUE' remplacé par 'AVE'
4) Numéro de rue écrit sous leur forme chiffré.'''
address = address.upper()
address = address.replace('.','')
address = address.replace('STREET', 'ST')
address = address.replace('AVENUE', 'AVE')
address = address.replace('FIRST', '1ST')
address = address.replace('SECOND', '2ND')
address = address.replace('THIRD', '3RD')
address = address.replace('FOURTH', '4TH')
address = address.replace('FIFTH', '5TH')
return address
df['Address'] = df['Address'].apply(lambda col: normAddress(col))
df.YearsENERGYSTARCertified.value_counts()
2016 25 2009 8 2013 7 2017 6 2016, 2015 6 20162015 4 2014 4 2010 4 20172016 3 2015 3 2011 3 2007 2 201620092008 2 201720162015 2 2011, 2009 2 2016, 2009, 2008 2 20112009 2 20172015 2 2017, 2015 2 2012 2 2009, 2005 2 20092005 2 2016, 2015, 2012, 2009 1 2017, 2016, 2012 1 201220092008 1 20162012 1 2016, 2015, 2014, 2013, 2012, 2010, 2009 1 2012, 2010, 2009, 2008 1 2014201320112010200920082007 1 20162011201020092008 1 2016, 2015, 2014, 2013, 2011, 2009 1 2017201620152012200920072006 1 2015, 2012 1 2017, 2011, 2010, 2007 1 2017201520142013201120092008 1 201520142013 1 2016201120102009 1 2017201520142013 1 2010, 2009, 2007 1 2016, 2015, 2014, 2013, 2009, 2008 1 2016, 2014, 2012 1 2017, 2016, 2014, 2013, 2012, 2011, 2010, 2009, 2007 1 201720162014201320122011201020092007 1 2015, 2013, 2012, 2010 1 2016, 2015, 2013, 2012, 2011, 2010, 2009, 2008 1 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004 1 2016, 2015, 2014, 2012, 2008 1 2016, 2012 1 2016, 2015, 2012, 2009, 2007, 2006 1 2015201320122010 1 2016, 2015, 2014, 2013, 2011, 2008 1 201720152014201220102008 1 2015, 2014, 2013, 2011, 2009, 2008 1 201620152014201320092008 1 201720162015201420132008 1 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008 1 2015, 2014, 2012, 2010, 2008 1 2017, 2015, 2014, 2013, 2011 1 2016, 2014, 2012, 2011, 2008, 2007 1 201620152014 1 20162015201320122010 1 2017201620152014201320122011201020092008 1 2017201120102007 1 2016, 2015, 2010, 2009, 2008 1 2016, 2015, 2014, 2013, 2011, 2010, 2009, 2007, 2000 1 201620142012 1 2016, 2015, 2012 1 2012, 2010 1 20172015201420132011 1 2017, 2015, 2014, 2013 1 201620152014201320112009 1 201720102007 1 201620152014201320112010200920072000 1 201620142012201120082007 1 2014, 2013, 2011, 2010, 2009, 2008, 2007 1 2012, 2010, 2007, 2006 1 2016, 2015, 2014 1 20152014201220112009 1 2016201320122011201020092008 1 20162015201020092008 1 2016201520142013201220102009 1 2017201620152014201320112008 1 2016, 2011, 2010, 2009 1 201720162012 1 2012, 2009, 2008 1 2010, 2008 1 2016, 2015, 2014, 2013, 2008 1 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2003, 2002, 2000 1 2014201020082001 1 2014201320122008 1 2016, 2015, 2014, 2013, 2012, 2010, 2009, 2008 1 2016201520142013201220112010 1 2017201320092008 1 2016201520142013201220112010200920082007200620052004 1 2016, 2013, 2012, 2011, 2010, 2009, 2008 1 2016, 2015, 2013, 2012, 2010 1 2015, 2014, 2012, 2011, 2009 1 20152012 1 2010, 2007 1 201020092007 1 201620152012 1 2012201020072006 1 20102008 1 2016201520122009 1 20172016201520092008 1 2013, 2009, 2008 1 20162015201320122011201020092008 1 20162015201420132012201020092008 1 2016201520142013 1 2016, 2015, 2009, 2008 1 201620152014201320122011201020092008200720062005200320022000 1 20162015201420122008 1 Name: YearsENERGYSTARCertified, dtype: int64
Les entrées (des années) sont soit séparées par une virgule,
soient collées les unes aux autres sans séparateur.
Je formate cette colonne et transforme les données en *List* :
def insert_char(myStr, group=4, char=','):
'''Si <myStr> est Null, retourne np.nan
Sinon Si <char> n'est pas présent dans <myStr>,
on insère <char> tous les <group> caractères de la chaîne <myStr>
et on retourne le résultat au format List
Sinon on supprime les espaces de <myStr> et on retourne
le résultat au format List'''
if pd.isna(myStr):
return np.nan
else:
myStr = str(myStr)
if not char in myStr:
return [int(i) for i in char.join(myStr[i:i+group] for i in range(0, len(myStr), group)).split(",")]
else:
return [int(i) for i in myStr.replace(" ", "").split(",")]
df.YearsENERGYSTARCertified = df.YearsENERGYSTARCertified.apply(lambda col: insert_char(col))
Quelques exemples du résultat :
df.YearsENERGYSTARCertified[df.YearsENERGYSTARCertified.notna()].tail(10)
6175 [2017, 2016] 6191 [2016] 6434 [2016, 2015, 2014, 2013, 2012, 2010, 2009, 2008] 6435 [2017, 2016, 2015] 6436 [2016, 2015, 2010, 2009, 2008] 6456 [2013] 6458 [2013] 6539 [2016, 2009, 2008] 6621 [2017, 2016] 6657 [2017] Name: YearsENERGYSTARCertified, dtype: object
Je supprime l'encodage "\n" présent dans certaines occurrences :
df.PrimaryPropertyType.value_counts()
Small- and Mid-Sized Office 582 Other 491 Large Office 338 K-12 School 254 Mixed Use Property 217 Retail Store 189 Warehouse 187 Non-Refrigerated Warehouse 187 Hotel 149 Worship Facility 143 Medical Office 82 Distribution Center 55 Distribution Center\n 51 Senior Care Community 39 Supermarket / Grocery Store 39 Supermarket/Grocery Store 36 Self-Storage Facility 29 Self-Storage Facility\n 27 Refrigerated Warehouse 25 College/University 21 Residence Hall 21 University 21 Hospital 20 Residence Hall/Dormitory 15 Restaurant 13 Laboratory 11 Restaurant\n 10 Low-Rise Multifamily 5 SPS-District K-12 4 Office 3 Name: PrimaryPropertyType, dtype: int64
df.PrimaryPropertyType = df.PrimaryPropertyType.apply(lambda col: col.replace("\n", ""))
df.PrimaryPropertyType.value_counts()
Small- and Mid-Sized Office 582 Other 491 Large Office 338 K-12 School 254 Mixed Use Property 217 Retail Store 189 Warehouse 187 Non-Refrigerated Warehouse 187 Hotel 149 Worship Facility 143 Distribution Center 106 Medical Office 82 Self-Storage Facility 56 Senior Care Community 39 Supermarket / Grocery Store 39 Supermarket/Grocery Store 36 Refrigerated Warehouse 25 Restaurant 23 College/University 21 Residence Hall 21 University 21 Hospital 20 Residence Hall/Dormitory 15 Laboratory 11 Low-Rise Multifamily 5 SPS-District K-12 4 Office 3 Name: PrimaryPropertyType, dtype: int64
df.Neighborhood.value_counts()
DOWNTOWN 714 GREATER DUWAMISH 684 LAKE UNION 293 MAGNOLIA / QUEEN ANNE 292 EAST 242 NORTHEAST 238 NORTHWEST 165 BALLARD 131 NORTH 120 CENTRAL 100 SOUTHEAST 91 DELRIDGE 86 SOUTHWEST 80 North 9 Ballard 6 Northwest 5 Central 4 Delridge 3 DELRIDGE NEIGHBORHOODS 1 Name: Neighborhood, dtype: int64
df.Neighborhood = df.Neighborhood.apply(lambda col: col.upper())
df.loc[df.Neighborhood.str.contains('DELRIDGE'),'Neighborhood'] = 'DELRIDGE'
df.Neighborhood.value_counts()
DOWNTOWN 714 GREATER DUWAMISH 684 LAKE UNION 293 MAGNOLIA / QUEEN ANNE 292 EAST 242 NORTHEAST 238 NORTHWEST 170 BALLARD 137 NORTH 129 CENTRAL 104 SOUTHEAST 91 DELRIDGE 90 SOUTHWEST 80 Name: Neighborhood, dtype: int64
df.Outlier.value_counts()
Low Outlier 15 Low outlier 14 High Outlier 14 High outlier 2 Name: Outlier, dtype: int64
df.Outlier = df.Outlier.map({'High Outlier':'High Outlier',
'High outlier':'High Outlier',
'Low Outlier':'Low Outlier',
'Low outlier':'Low Outlier'})
df.Outlier.value_counts()
Low Outlier 29 High Outlier 16 Name: Outlier, dtype: int64
df[['ListOfAllPropertyUseTypes','LargestPropertyUseType','SecondLargestPropertyUseType','ThirdLargestPropertyUseType']]
| ListOfAllPropertyUseTypes | LargestPropertyUseType | SecondLargestPropertyUseType | ThirdLargestPropertyUseType | |
|---|---|---|---|---|
| 0 | Hotel | Hotel | NaN | NaN |
| 1 | Hotel, Parking, Restaurant | Hotel | Parking | Restaurant |
| 2 | Hotel, Parking, Swimming Pool | Hotel | Parking | Swimming Pool |
| 3 | Hotel | Hotel | NaN | NaN |
| 4 | Hotel, Parking, Swimming Pool | Hotel | Parking | Swimming Pool |
| ... | ... | ... | ... | ... |
| 6711 | Office | Office | NaN | NaN |
| 6712 | Other - Recreation | Other - Recreation | NaN | NaN |
| 6713 | Fitness Center/Health Club/Gym, Other - Recrea... | Other - Recreation | Fitness Center/Health Club/Gym | Swimming Pool |
| 6714 | Fitness Center/Health Club/Gym, Food Service, ... | Other - Recreation | Fitness Center/Health Club/Gym | Pre-school/Daycare |
| 6715 | Fitness Center/Health Club/Gym, Food Service, ... | Other - Recreation | Fitness Center/Health Club/Gym | Pre-school/Daycare |
3264 rows × 4 columns
df['ListOfAllPropertyUseTypes'].dropna().unique()
array(['Hotel', 'Hotel, Parking, Restaurant',
'Hotel, Parking, Swimming Pool', 'Police Station',
'Other - Entertainment/Public Assembly', 'Hotel, Parking',
'Data Center, Library, Parking',
'Fitness Center/Health Club/Gym, Office, Swimming Pool',
'Office, Other, Other - Lodging/Residential, Restaurant, Social/Meeting Hall',
'Courthouse', 'Other', 'K-12 School', 'College/University',
'Automobile Dealership',
'Hotel, Parking, Retail Store, Swimming Pool',
'K-12 School, Multifamily Housing, Office, Other - Entertainment/Public Assembly',
'Self-Storage Facility', 'Non-Refrigerated Warehouse, Office',
'Office, Other - Education',
'Office, Self-Storage Facility, Vocational School',
'Data Center, Office, Retail Store', 'Office, Retail Store',
'Office, Other - Entertainment/Public Assembly, Restaurant',
'Hotel, Parking, Restaurant, Retail Store',
'Hotel, Other - Entertainment/Public Assembly, Retail Store',
'Hotel, Restaurant', 'Office', 'Parking, Senior Care Community',
'Senior Care Community', 'Medical Office',
'Data Center, Medical Office, Non-Refrigerated Warehouse, Office, Other, Parking',
'Retail Store', 'Office, Other, Parking, Retail Store',
'Office, Other, Parking',
'Data Center, Distribution Center, Office, Parking, Retail Store',
'Hotel, Swimming Pool', 'Office, Parking', 'Other, Parking',
'Hospital (General Medical & Surgical), Parking', 'Office, Other',
'Museum', 'Data Center, Office',
'Repair Services (Vehicle, Shoe, Locksmith, etc)',
'Food Service, Office, Other - Lodging/Residential',
'Residence Hall/Dormitory', 'K-12 School, Parking',
'Courthouse, Other',
'Non-Refrigerated Warehouse, Office, Other - Services',
'Restaurant, Retail Store', 'Other/Specialty Hospital',
'Hospital (General Medical & Surgical)',
'Financial Office, Medical Office, Parking',
'Non-Refrigerated Warehouse',
'Non-Refrigerated Warehouse, Office, Other - Services, Residence Hall/Dormitory',
'Data Center, Distribution Center, Office, Other, Parking',
'Office, Restaurant, Self-Storage Facility',
'Other - Entertainment/Public Assembly, Parking',
'Data Center, Distribution Center, Office, Other',
'Non-Refrigerated Warehouse, Office, Other, Other - Services',
'Food Service, Hotel, Parking', 'Laboratory, Office, Parking',
'Fast Food Restaurant, Medical Office, Movie Theater, Parking, Retail Store',
'Financial Office, Multifamily Housing, Parking, Retail Store, Supermarket/Grocery Store',
'Hotel, Other, Parking, Restaurant, Retail Store',
'Non-Refrigerated Warehouse, Parking',
'K-12 School, Medical Office, Office, Restaurant, Retail Store, Senior Care Community',
'Office, Restaurant', 'Worship Facility',
'Office, Other, Other - Entertainment/Public Assembly',
'Office, Residence Hall/Dormitory, Retail Store',
'Medical Office, Office', 'Other, Restaurant, Retail Store',
'Non-Refrigerated Warehouse, Other - Services',
'Financial Office, Laboratory, Office, Parking',
'Office, Other, Parking, Restaurant',
'Office, Parking, Restaurant',
'Office, Other - Recreation, Parking, Retail Store',
'Fitness Center/Health Club/Gym, Office, Other, Parking, Restaurant',
'Office, Retail Store, Social/Meeting Hall',
'Convenience Store without Gas Station, Food Service, Office',
'Office, Parking, Pre-school/Daycare',
'Convenience Store without Gas Station, Office, Parking',
'Data Center, Office, Parking',
'Financial Office, Food Sales, Office, Other, Parking',
'Convenience Store without Gas Station, Data Center, Financial Office, Fitness Center/Health Club/Gym, Office, Other, Parking, Personal Services (Health/Beauty, Dry Cleaning, etc), Restaurant',
'Non-Refrigerated Warehouse, Office, Retail Store',
'Data Center, Office, Other',
'Fitness Center/Health Club/Gym, Office',
'Laboratory, Office, Other, Parking', 'Supermarket/Grocery Store',
'Office, Other, Restaurant',
'Office, Other - Restaurant/Bar, Retail Store, Single Family Home',
'Non-Refrigerated Warehouse, Other',
'Office, Performing Arts, Retail Store',
'Office, Other - Restaurant/Bar, Parking',
'Office, Other - Education, Parking',
'Financial Office, Office, Other, Parking, Restaurant',
'Hotel, Office, Retail Store',
'Distribution Center, Non-Refrigerated Warehouse, Office',
'College/University, Office, Restaurant',
'Financial Office, Office, Other, Parking, Restaurant, Retail Store',
'Office, Other, Retail Store', 'Office, Parking, Retail Store',
'Office, Restaurant, Retail Store',
'Bank Branch, Office, Other, Parking, Restaurant',
'Medical Office, Office, Supermarket/Grocery Store',
'Food Sales, Multifamily Housing, Office, Other - Education, Other - Restaurant/Bar, Pre-school/Daycare, Restaurant, Retail Store, Supermarket/Grocery Store',
'Office, Other - Restaurant/Bar, Parking, Retail Store',
'Movie Theater, Other - Restaurant/Bar, Parking',
'Laboratory, Office',
'Data Center, Office, Parking, Restaurant, Retail Store',
'Other, Retail Store', 'Convention Center, Parking',
'Urgent Care/Clinic/Other Outpatient', 'Medical Office, Parking',
'Data Center, Financial Office, Office, Other, Parking, Restaurant',
'Data Center, Office, Other, Parking, Restaurant',
'Distribution Center, Office', 'Distribution Center',
'Adult Education, Bank Branch, Office, Parking',
'Data Center, Office, Other, Restaurant',
'Parking, Retail Store, Supermarket/Grocery Store',
'Data Center, Laboratory', 'Laboratory',
'Financial Office, Food Service, Office, Other, Parking',
'Medical Office, Office, Parking',
'Fitness Center/Health Club/Gym, Office, Parking, Restaurant',
'Bank Branch, Office, Parking',
'Non-Refrigerated Warehouse, Office, Other, Other - Entertainment/Public Assembly, Parking',
'Office, Other - Services', 'Parking, Strip Mall',
'Wholesale Club/Supercenter',
'Retail Store, Supermarket/Grocery Store',
'Parking, Supermarket/Grocery Store',
'Data Center, Library, Non-Refrigerated Warehouse, Office, Other - Entertainment/Public Assembly, Other - Recreation, Other - Services, Restaurant, Social/Meeting Hall',
'Multifamily Housing, Non-Refrigerated Warehouse, Office, Parking, Retail Store',
'Parking, Retail Store', 'Parking, Self-Storage Facility',
'Distribution Center, Parking',
'Office, Parking, Refrigerated Warehouse',
'Office, Other - Services, Parking', 'Strip Mall',
'Medical Office, Retail Store', 'Refrigerated Warehouse',
'Multifamily Housing, Parking, Restaurant, Retail Store',
'Food Service, Office, Parking, Restaurant',
'Data Center, Office, Other, Other - Entertainment/Public Assembly',
'Non-Refrigerated Warehouse, Retail Store',
'Distribution Center, Office, Other',
'Multifamily Housing, Non-Refrigerated Warehouse, Office, Parking',
'Distribution Center, Office, Parking',
'Distribution Center, Non-Refrigerated Warehouse, Office, Retail Store',
'Distribution Center, Office, Other, Other - Recreation, Parking',
'Medical Office, Other, Parking',
'Financial Office, Hotel, Multifamily Housing, Other, Parking, Restaurant, Supermarket/Grocery Store',
'Data Center, Office, Restaurant',
'Data Center, Financial Office, Office, Parking, Restaurant',
'Other - Recreation, Parking, Swimming Pool', 'Lifestyle Center',
'Data Center, Office, Parking, Restaurant',
'Non-Refrigerated Warehouse, Office, Other, Other - Recreation, Other - Services, Restaurant',
'Adult Education, Fitness Center/Health Club/Gym, Office, Other - Restaurant/Bar, Parking, Retail Store',
'Non-Refrigerated Warehouse, Office, Other',
'Distribution Center, Office, Retail Store',
'Other - Public Services, Parking',
'Multifamily Housing, Non-Refrigerated Warehouse, Office, Parking, Retail Store, Worship Facility',
'Fitness Center/Health Club/Gym, Office, Parking',
'Data Center, Office, Other, Parking',
'Bar/Nightclub, Office, Personal Services (Health/Beauty, Dry Cleaning, etc)',
'Medical Office, Parking, Retail Store',
'Office, Other - Entertainment/Public Assembly, Other - Mall, Restaurant',
'Office, Performing Arts, Restaurant, Retail Store, Self-Storage Facility',
'Office, Other, Other - Restaurant/Bar, Parking',
'Financial Office, Food Service, Office, Other',
'Data Center, Medical Office, Office, Parking, Restaurant',
'College/University, Parking',
'Data Center, Non-Refrigerated Warehouse, Office, Retail Store',
'Non-Refrigerated Warehouse, Other, Parking, Retail Store',
'Other, Parking, Restaurant, Retail Store',
'Distribution Center, Non-Refrigerated Warehouse, Refrigerated Warehouse',
'Non-Refrigerated Warehouse, Office, Other - Services, Retail Store',
'Multifamily Housing, Office, Parking, Retail Store',
'Non-Refrigerated Warehouse, Other, Restaurant, Retail Store',
'Non-Refrigerated Warehouse, Office, Other - Recreation',
'Medical Office, Office, Other',
'Other - Entertainment/Public Assembly, Restaurant',
'Data Center, Non-Refrigerated Warehouse, Other, Refrigerated Warehouse, Repair Services (Vehicle, Shoe, Locksmith, etc), Restaurant',
'Automobile Dealership, Office',
'Manufacturing/Industrial Plant, Office, Parking',
'Manufacturing/Industrial Plant',
'Office, Other, Retail Store, Swimming Pool',
'Medical Office, Office, Other, Other - Services, Parking, Retail Store',
'Parking, Restaurant', 'Social/Meeting Hall', 'Restaurant',
'Office, Other - Lodging/Residential', 'Bank Branch, Office',
'Other - Education', 'K-12 School, Worship Facility',
'Bank Branch, Other, Parking', 'Financial Office', 'Fire Station',
'Other - Entertainment/Public Assembly, Parking, Social/Meeting Hall, Swimming Pool',
'Non-Refrigerated Warehouse, Office, Other, Retail Store',
'Non-Refrigerated Warehouse, Other, Retail Store',
'Bank Branch, Financial Office',
'Bank Branch, Senior Care Community', 'Parking, Worship Facility',
'Medical Office, Multifamily Housing, Office, Restaurant, Retail Store',
'Multifamily Housing, Other - Entertainment/Public Assembly',
'Other, Worship Facility', 'Parking, Residence Hall/Dormitory',
'Office, Residence Hall/Dormitory', 'Office, Social/Meeting Hall',
'Performing Arts',
'Non-Refrigerated Warehouse, Office, Other - Recreation, Restaurant',
'Other - Recreation, Parking, Restaurant',
'Fitness Center/Health Club/Gym, Swimming Pool',
'Food Service, Other - Services, Retail Store, Supermarket/Grocery Store',
'Other - Mall, Restaurant',
'Distribution Center, Office, Refrigerated Warehouse',
'K-12 School, Office, Retail Store', 'Other - Recreation',
'Other - Services',
'Fitness Center/Health Club/Gym, Office, Other - Recreation, Pre-school/Daycare',
'Non-Refrigerated Warehouse, Office, Self-Storage Facility',
'Automobile Dealership, Parking',
'Non-Refrigerated Warehouse, Other - Recreation',
'Multifamily Housing, Office, Other - Mall, Parking, Restaurant',
'Hotel, Retail Store',
'Bank Branch, Bar/Nightclub, Office, Parking',
'Multifamily Housing, Office', 'Residential Care Facility',
'Bank Branch', 'Other - Education, Parking',
'Food Service, Office, Other - Entertainment/Public Assembly',
'Office, Other, Other - Services, Residence Hall/Dormitory, Restaurant',
'Food Service, Office, Parking',
'Bank Branch, Office, Parking, Restaurant',
'Other - Entertainment/Public Assembly, Restaurant, Retail Store',
'Other, Personal Services (Health/Beauty, Dry Cleaning, etc), Restaurant, Retail Store',
'Bar/Nightclub, Food Sales, Multifamily Housing, Restaurant, Retail Store, Self-Storage Facility',
'Food Sales, Office, Restaurant',
'Food Sales, Manufacturing/Industrial Plant, Office, Other - Entertainment/Public Assembly, Other - Restaurant/Bar, Restaurant, Retail Store, Self-Storage Facility',
'Multifamily Housing, Office, Restaurant', 'Food Service',
'Hotel, Multifamily Housing, Parking, Retail Store',
'Medical Office, Office, Retail Store',
'Other - Recreation, Self-Storage Facility',
'Office, Repair Services (Vehicle, Shoe, Locksmith, etc), Retail Store',
'Adult Education, Parking',
'Medical Office, Non-Refrigerated Warehouse',
'Office, Worship Facility', 'Office, Other - Recreation',
'Medical Office, Non-Refrigerated Warehouse, Office, Retail Store',
'Bank Branch, Medical Office, Parking, Retail Store',
'Library, Other - Services',
'Distribution Center, Non-Refrigerated Warehouse',
'Multifamily Housing, Office, Other, Strip Mall',
'Fitness Center/Health Club/Gym, Other - Recreation, Swimming Pool',
'Office, Parking, Self-Storage Facility',
'Office, Repair Services (Vehicle, Shoe, Locksmith, etc)',
'Medical Office, Office, Worship Facility',
'Medical Office, Other/Specialty Hospital, Outpatient Rehabilitation/Physical Therapy, Parking',
'Adult Education', 'Museum, Parking',
'Bank Branch, Multifamily Housing, Office, Other, Retail Store',
'Office, Other - Utility, Parking',
'Office, Restaurant, Supermarket/Grocery Store',
'Multifamily Housing, Non-Refrigerated Warehouse, Office, Retail Store',
'Other - Recreation, Worship Facility',
'Non-Refrigerated Warehouse, Office, Other, Parking, Retail Store',
'Medical Office, Non-Refrigerated Warehouse, Office, Other',
'Other - Lodging/Residential, Parking',
'Manufacturing/Industrial Plant, Parking',
'Office, Refrigerated Warehouse',
'Office, Other - Public Services',
'Non-Refrigerated Warehouse, Parking, Repair Services (Vehicle, Shoe, Locksmith, etc), Retail Store',
'Food Service, Non-Refrigerated Warehouse, Retail Store',
'Fitness Center/Health Club/Gym, Office, Other - Recreation',
'Distribution Center, Non-Refrigerated Warehouse, Office, Other - Education, Parking',
'Movie Theater',
'Fast Food Restaurant, Other, Other - Entertainment/Public Assembly',
'College/University, Medical Office',
'Fitness Center/Health Club/Gym, Office, Other - Recreation, Other - Technology/Science, Pre-school/Daycare, Swimming Pool',
'Food Service, Other, Other - Education, Other - Entertainment/Public Assembly, Other - Recreation, Social/Meeting Hall',
'Other, Other - Entertainment/Public Assembly, Self-Storage Facility',
'Other, Refrigerated Warehouse',
'Non-Refrigerated Warehouse, Office, Social/Meeting Hall',
'Non-Refrigerated Warehouse, Other, Refrigerated Warehouse',
'Other - Mall, Parking',
'Non-Refrigerated Warehouse, Worship Facility',
'Outpatient Rehabilitation/Physical Therapy, Parking',
'Personal Services (Health/Beauty, Dry Cleaning, etc)',
'Non-Refrigerated Warehouse, Office, Repair Services (Vehicle, Shoe, Locksmith, etc)',
'Hotel, Multifamily Housing, Parking',
'College/University, Restaurant',
'Office, Other, Refrigerated Warehouse',
'Other, Parking, Restaurant',
'Multifamily Housing, Office, Other, Parking',
'Financial Office, Multifamily Housing, Parking, Residence Hall/Dormitory',
'Office, Restaurant, Retail Store, Self-Storage Facility',
'Bar/Nightclub, Multifamily Housing',
'Office, Other, Worship Facility',
'Food Sales, Non-Refrigerated Warehouse, Office',
'Multifamily Housing, Office, Other - Entertainment/Public Assembly, Parking',
'Medical Office, Office, Other, Restaurant, Retail Store',
'Parking, Senior Care Community, Worship Facility',
'Multifamily Housing, Office, Parking, Restaurant',
'Office, Parking, Residence Hall/Dormitory, Restaurant, Social/Meeting Hall',
'Office, Other, Other - Entertainment/Public Assembly, Other - Recreation, Other - Utility, Restaurant, Retail Store',
'Bank Branch, Parking', 'Office, Other - Restaurant/Bar',
'Manufacturing/Industrial Plant, Office, Retail Store',
'Food Service, Library, Office, Parking',
'K-12 School, Office, Supermarket/Grocery Store, Worship Facility',
'Bar/Nightclub, Food Sales, Museum, Retail Store, Supermarket/Grocery Store',
'Office, Other - Entertainment/Public Assembly',
'Other - Lodging/Residential',
'Multifamily Housing, Retail Store, Social/Meeting Hall',
'Multifamily Housing, Office, Other',
'Fitness Center/Health Club/Gym, Office, Other - Recreation, Other - Technology/Science, Parking, Pre-school/Daycare',
'Other - Education, Performing Arts, Social/Meeting Hall',
'Office, Supermarket/Grocery Store',
'Fitness Center/Health Club/Gym, Medical Office, Office, Parking, Restaurant',
'Non-Refrigerated Warehouse, Office, Parking',
'Non-Refrigerated Warehouse, Office, Restaurant, Retail Store',
'Office, Other - Entertainment/Public Assembly, Restaurant, Urgent Care/Clinic/Other Outpatient',
'Non-Refrigerated Warehouse, Office, Refrigerated Warehouse',
'Other - Education, Worship Facility', 'Library, Parking',
'Multifamily Housing, Parking, Supermarket/Grocery Store',
'Other - Services, Retail Store',
'K-12 School, Office, Social/Meeting Hall, Worship Facility',
'Multifamily Housing, Parking, Retail Store', 'Laboratory, Other',
'Distribution Center, Non-Refrigerated Warehouse, Supermarket/Grocery Store',
'Enclosed Mall, Manufacturing/Industrial Plant, Office, Other - Restaurant/Bar',
'Multifamily Housing, Office, Parking', 'Other - Utility',
'Hospital (General Medical & Surgical), Other',
'Hotel, Parking, Retail Store',
'Data Center, Multifamily Housing, Office, Other, Parking',
'Data Center, Parking',
'Convenience Store without Gas Station, Laboratory, Parking',
'Lifestyle Center, Medical Office', 'Parking, Pre-school/Daycare',
'Fitness Center/Health Club/Gym, Office, Other - Education, Other - Recreation, Other - Technology/Science, Pre-school/Daycare, Swimming Pool',
'Office, Other - Public Services, Personal Services (Health/Beauty, Dry Cleaning, etc), Senior Care Community',
'Pre-school/Daycare',
'Energy/Power Station, Laboratory, Manufacturing/Industrial Plant, Non-Refrigerated Warehouse, Office',
'Other - Mall, Personal Services (Health/Beauty, Dry Cleaning, etc)',
'Laboratory, Non-Refrigerated Warehouse, Office, Other - Utility',
'Financial Office, Office, Parking, Retail Store', 'Data Center',
'Office, Parking, Personal Services (Health/Beauty, Dry Cleaning, etc)',
'Data Center, Office, Parking, Performing Arts, Restaurant, Retail Store',
'Fitness Center/Health Club/Gym, Office, Other - Education, Other - Recreation, Other - Restaurant/Bar, Outpatient Rehabilitation/Physical Therapy, Personal Services (Health/Beauty, Dry Cleaning, etc), Restaurant, Retail Store, Self-Storage Facility, Vocat',
'Enclosed Mall, Office',
'Office, Other - Restaurant/Bar, Other - Technology/Science, Parking',
'Office, Other - Technology/Science, Parking',
'Hotel, Parking, Restaurant, Swimming Pool',
'Laboratory, Medical Office',
'Manufacturing/Industrial Plant, Office, Parking, Retail Store',
'Multifamily Housing',
'Bank Branch, Parking, Senior Care Community',
'Food Service, Retail Store, Supermarket/Grocery Store',
'Food Sales, Office, Restaurant, Retail Store, Self-Storage Facility, Social/Meeting Hall',
'Data Center, Non-Refrigerated Warehouse, Office',
'Other, Supermarket/Grocery Store', 'Bank Branch, Retail Store',
'Pre-school/Daycare, Worship Facility',
'Fitness Center/Health Club/Gym, Office, Other, Other - Utility, Restaurant, Retail Store',
'Office, Other - Entertainment/Public Assembly, Other - Public Services, Restaurant, Urgent Care/Clinic/Other Outpatient',
'Office, Other, Other - Restaurant/Bar, Strip Mall',
'Multifamily Housing, Parking, Personal Services (Health/Beauty, Dry Cleaning, etc), Retail Store, Supermarket/Grocery Store',
'Residence Hall/Dormitory, Supermarket/Grocery Store',
'Movie Theater, Office, Vocational School',
'Multifamily Housing, Office, Parking, Restaurant, Retail Store',
'Data Center, Laboratory, Museum, Office, Other, Parking',
'Fitness Center/Health Club/Gym, Office, Other - Recreation, Pre-school/Daycare, Social/Meeting Hall',
'Prison/Incarceration',
'Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/Daycare',
'Fitness Center/Health Club/Gym, Office, Other - Recreation, Swimming Pool',
'Fitness Center/Health Club/Gym, Office, Other - Recreation, Other - Technology/Science'],
dtype=object)
La colonne 'ListOfAllPropertyUseTypes' contient des chaînes de caractère
contenant des éléments séparés par une virgule.
Je crée une fonction permettant de convertir cette colonne au format List
en gérant également les cas particuliers où des virgules peuvent être à l'intérieur d'éléments.
Il ne faut donc pas les considérer comme des séparateurs.
def ConvToListColumnListOfAllPropertyUseTypes(dataLOAPUT):
'''Retourne sous forme de liste chaque enregistrement
de la colonne ListOfAllPropertyUseTypes (LOAPUT)en prenant
en compte les erreurs possibles dues aux virgules incluses
dans certains enregistrements'''
# Listes spéciales contenant les éléments mal splités par une virgule ou contenant des parenthèses
specialList = ['Hospital (General Medical & Surgical)',
'Personal Services (Health/Beauty, Dry Cleaning, etc)',
'Repair Services (Vehicle, Shoe, Locksmith, etc)']
# Liste des élements à retirer car ajouté par erreur
errorList = ['Dry Cleaning', 'Shoe', 'Locksmith', 'etc)']
myList = []
if pd.notna(dataLOAPUT):
for i in dataLOAPUT.split(','):
if i.strip() not in myList and not '(' in i :
myList.append(i.strip())
else:
for j in specialList:
if (i.strip() in j) and (j not in myList):
myList.append(j)
return [k for k in myList if k not in errorList]
Je convertis sous forme de List les valeurs contenu dans 'ListOfAllPropertyUseTypes'
df['ListOfAllPropertyUseTypes'] = df['ListOfAllPropertyUseTypes'].\
apply(lambda ele: ConvToListColumnListOfAllPropertyUseTypes(ele))
Voici décrit ci-dessous les deux données, normalement calculées par les agents de terrain,
que nous allons chercher à prédire à partir des données récoltées par les agents en 2015 et 2016.
'**TotalGHGEmissions**' :
'**SiteEnergyUse**' :
Pour information :
Pour plus de lisibilité, je réarrange les colonnes du DataFrame
et place les colonnes Targets sur la droite :
df = df[[col for col in df if col not in ['TotalGHGEmissions','SiteEnergyUse']] + ['TotalGHGEmissions','SiteEnergyUse']]
Maintenant que nos données sont correctement formatées,
attardons nous un peu sur les caractéristiques de notre DataFrame '**df**' :
df.shape
(3264, 33)
df.dtypes.value_counts()
float64 15 object 11 int64 6 bool 1 dtype: int64
plt.figure(figsize = (8, 8))
x = df.dtypes.value_counts().sort_index()
plt.pie(x, labels = ['bool', 'int64', 'float64', 'object'],
textprops={'fontsize': 18},
colors = ['green', 'lightgreen', 'orange', 'firebrick'],
autopct = lambda x: str(round(x, 2)) + '%',
pctdistance = 0.7, labeldistance = 1.05,
shadow = True)
plt.legend()
<matplotlib.legend.Legend at 0x2516d8a7a90>
Je répartie mes colonnes en 3 catégories :
Ordre_Distance = ['ageBuilding',
'NumberofFloors',
'PropertyGFATotal',
'PropertyGFAParking',
'PropertyGFABuilding',
'LargestPropertyUseTypeGFA',
'SecondLargestPropertyUseTypeGFA',
'ThirdLargestPropertyUseTypeGFA',
'ENERGYSTARScore',
'2010 Census Tracts',
'Latitude',
'Longitude',
'Proportion_Electricity',
'Proportion_NaturalGas',
'Proportion_Steam',
'TotalGHGEmissions',
'SiteEnergyUse',]
NoDistance_Ordre = ['Outlier','DefaultData']
NoDistance_NoOrdre = ['BuildingType',
'PrimaryPropertyType',
'CouncilDistrictCode',
'Neighborhood',
'ComplianceStatus',
'City Council Districts',
'Address',
'ZipCode',
'YearsENERGYSTARCertified',
'ListOfAllPropertyUseTypes',
'LargestPropertyUseType',
'SecondLargestPropertyUseType',
'ThirdLargestPropertyUseType']
f, axes = plt.subplots(figsize=(15, 7))
sns.despine(left=True)
sns.distplot(df.TotalGHGEmissions, color='mediumslateblue')
plt.xlabel('TotalGHGEmissions', fontsize=18)
plt.ylabel('Distribution', fontsize=16)
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
Text(0, 0.5, 'Distribution')
f, axes = plt.subplots(figsize=(15, 7))
sns.despine(left=True)
sns.distplot(df.SiteEnergyUse, color='indianred')
plt.xlabel('SiteEnergyUse', fontsize=18)
plt.ylabel('Distribution', fontsize=16)
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
Text(0, 0.5, 'Distribution')
La distribution des targets est unimodale non centrée.
plt.figure(figsize=(20,10))
sns.heatmap(df.isna(), cbar=False)
<AxesSubplot:>
Affichage des colonnes et de leur *taux de vide* correspondant :
(df.isnull().sum()/df.shape[0]).sort_values(ascending=False)
Outlier 0.98621 City Council Districts 0.97549 2010 Census Tracts 0.97335 YearsENERGYSTARCertified 0.94332 ThirdLargestPropertyUseType 0.79259 ThirdLargestPropertyUseTypeGFA 0.79259 SecondLargestPropertyUseType 0.49387 SecondLargestPropertyUseTypeGFA 0.49387 ENERGYSTARScore 0.32690 LargestPropertyUseTypeGFA 0.02114 LargestPropertyUseType 0.02114 ZipCode 0.00490 NumberofFloors 0.00245 ListOfAllPropertyUseTypes 0.00000 CouncilDistrictCode 0.00000 PropertyGFABuilding 0.00000 Neighborhood 0.00000 PrimaryPropertyType 0.00000 PropertyGFAParking 0.00000 PropertyGFATotal 0.00000 BuildingType 0.00000 SiteEnergyUse 0.00000 TotalGHGEmissions 0.00000 DefaultData 0.00000 ComplianceStatus 0.00000 Address 0.00000 Latitude 0.00000 Longitude 0.00000 ageBuilding 0.00000 Proportion_Electricity 0.00000 Proportion_NaturalGas 0.00000 Proportion_Steam 0.00000 OSEBuildingID 0.00000 dtype: float64
Je choisis les stratégies suivantes pour renseigner mes valeurs manquantes :
df.Outlier = df.Outlier.fillna('No Outlier')
df.drop(['2010 Census Tracts', 'City Council Districts'], axis=1, inplace=True)
df.YearsENERGYSTARCertified = df.YearsENERGYSTARCertified.apply(lambda col: col if isinstance(col, list) else [])
Ci-dessous, je décris la stratégie adoptée, pour chaque enregistrement
de notre jeu de données, pour renseigner les 6 colonnes lorsque les champs sont vides.
Je ferai référence à des dictionnaires dont les définitions
et les explications seront présentées un peu plus bas.
Renseignement de la colonne '**LargestPropertyUseType**' :
Je renseigne les valeurs manquantes de la colonne 'LargestPropertyUseType'
à partir du contenu de la colonne 'PrimaryPropertyType'
et du dictionnaire dictLinkListEachPrimaryPT (que je nommerai dictKey ci-dessous
par souci de lisibilité).
Renseignement de la colonne '**SecondLargestPropertyUseType**' :
Je renseigne les valeurs manquantes de la colonne 'SecondLargestPropertyUseType'
à partir du contenu des colonnes 'NbListOfAllPropertyUseTypes', 'ListOfAllPropertyUseTypes',
'PrimaryPropertyType' et du dictionnaire dictKey
Renseignement de la colonne '**ThirdLargestPropertyUseType**' :
Renseignement des colonnes '**LargestPropertyUseTypeGFA**',
'**SecondLargestPropertyUseTypeGFA**' et '**ThirdLargestPropertyUseTypeGFA**' :
Je renseigne les valeurs manquantes des colonnes 'LargestPropertyUseTypeGFA',
'SecondLargestPropertyUseTypeGFA' et 'ThirdLargestPropertyUseTypeGFA'
à partir du contenu des colonnes correspondantes (sans GFA) et
du dictionnaire dictOfEachPropertyUses
(que je nommerai dictMedian ci-dessous par souci de lisibilité)
Je crée une colonne '**NbListOfAllPropertyUseTypes**' qui contient
le nombre d'éléments dans chaque List de '**ListOfAllPropertyUseTypes**' :
df['NbListOfAllPropertyUseTypes'] = df.ListOfAllPropertyUseTypes.apply(lambda row: len(row))
J'ajoute immédiatement cette nouvelle colonne dans la List [Ordre_Distance] :
Ordre_Distance.append('NbListOfAllPropertyUseTypes')
Je remplis un dictionnaire contenant comme clés l'ensemble des entrées existantes dans 'ListOfAllPropertyUseTypes'
et comme valeurs les médianes associées pour la colonne 'LargestPropertyUseTypeGPA'
Je renseigne 0 si aucune entrée n'existe pour une clé donnée dans la colonne 'LargestPropertyUseType'.
dictOfEachPropertyUses = {}
for i in df.ListOfAllPropertyUseTypes:
for j in i:
if j not in dictOfEachPropertyUses:
if np.isnan(df["LargestPropertyUseTypeGFA"][df["LargestPropertyUseType"]==j].median()):
dictOfEachPropertyUses[j] = 0
else:
dictOfEachPropertyUses[j] = df["LargestPropertyUseTypeGFA"][df["LargestPropertyUseType"]==j].median()
Affichage du contenu du *dictionnaire* '**dictOfEachPropertyUses**' :
dictOfEachPropertyUses
{'Hotel': 83591.0,
'Parking': 104922.0,
'Restaurant': 23373.0,
'Swimming Pool': 0,
'Police Station': 88830.0,
'Other - Entertainment/Public Assembly': 35886.0,
'Data Center': 60000.0,
'Library': 15150.0,
'Fitness Center/Health Club/Gym': 45000.0,
'Office': 49800.0,
'Other': 37510.0,
'Other - Lodging/Residential': 29820.0,
'Social/Meeting Hall': 20969.5,
'Courthouse': 537150.0,
'K-12 School': 55279.5,
'College/University': 84300.0,
'Automobile Dealership': 38787.0,
'Retail Store': 36940.0,
'Multifamily Housing': 21400.0,
'Self-Storage Facility': 38439.0,
'Non-Refrigerated Warehouse': 33462.0,
'Other - Education': 23904.0,
'Vocational School': 0,
'Senior Care Community': 56000.0,
'Medical Office': 62869.5,
'Distribution Center': 41308.0,
'Hospital (General Medical & Surgical)': 483520.0,
'Museum': 43000.0,
'Repair Services (Vehicle, Shoe, Locksmith, etc)': 22320.0,
'Food Service': 22710.0,
'Residence Hall/Dormitory': 27600.0,
'Other - Services': 20047.0,
'Other/Specialty Hospital': 46047.0,
'Financial Office': 28636.0,
'Laboratory': 83182.5,
'Fast Food Restaurant': 0,
'Movie Theater': 33392.0,
'Supermarket/Grocery Store': 41198.0,
'Worship Facility': 26210.0,
'Other - Recreation': 26241.0,
'Convenience Store without Gas Station': 0,
'Pre-school/Daycare': 42213.5,
'Food Sales': 0,
'Personal Services (Health/Beauty, Dry Cleaning, etc)': 25406.0,
'Other - Restaurant/Bar': 11569.5,
'Single Family Home': 0,
'Performing Arts': 34976.0,
'Bank Branch': 21206.5,
'Convention Center': 1072000.0,
'Urgent Care/Clinic/Other Outpatient': 50142.5,
'Adult Education': 40158.0,
'Strip Mall': 102983.0,
'Wholesale Club/Supercenter': 144776.0,
'Refrigerated Warehouse': 25340.0,
'Lifestyle Center': 200185.5,
'Other - Public Services': 42375.0,
'Bar/Nightclub': 0,
'Other - Mall': 29640.0,
'Manufacturing/Industrial Plant': 23000.0,
'Fire Station': 38939.0,
'Residential Care Facility': 25557.0,
'Outpatient Rehabilitation/Physical Therapy': 33332.0,
'Other - Utility': 83474.5,
'Other - Technology/Science': 0,
'Enclosed Mall': 0,
'Energy/Power Station': 0,
'Vocat': 0,
'Prison/Incarceration': 11685.0}
Je réalise la même action que précedement mais pour la colonne '**PrimaryPropertyType**' :
Il est à noter que les clés ne sont pas exactement les mêmes entre les deux dictionnaires**
dictOfEachPrimaryPropertyType = {}
for i in df.PrimaryPropertyType:
if i not in dictOfEachPrimaryPropertyType:
if np.isnan(df["LargestPropertyUseTypeGFA"][df["PrimaryPropertyType"]==i].median()):
dictOfEachPrimaryPropertyType[i] = 0
else:
dictOfEachPrimaryPropertyType[i] = df["LargestPropertyUseTypeGFA"][df["PrimaryPropertyType"]==i].median()
dictOfEachPrimaryPropertyType
{'Hotel': 82731.0,
'Other': 38787.0,
'Mixed Use Property': 18635.5,
'K-12 School': 55279.5,
'College/University': 90322.0,
'Small- and Mid-Sized Office': 32610.0,
'Self-Storage Facility': 38439.0,
'Distribution Center': 41369.0,
'Large Office': 162975.5,
'Senior Care Community': 56000.0,
'Medical Office': 74230.5,
'Retail Store': 40000.0,
'Hospital': 483520.0,
'Residence Hall/Dormitory': 26000.0,
'Non-Refrigerated Warehouse': 35344.5,
'SPS-District K-12': 0,
'Worship Facility': 26210.0,
'Supermarket/Grocery Store': 41447.0,
'Laboratory': 83182.5,
'Refrigerated Warehouse': 25340.0,
'Restaurant': 22719.0,
'Low-Rise Multifamily': 22669.0,
'University': 84300.0,
'Warehouse': 35289.0,
'Residence Hall': 33448.0,
'Supermarket / Grocery Store': 42760.0,
'Office': 13661.0}
Les clés des deux dictionnaires étant différentes, je crée le dictionnaire 'dictLinkListEachPrimaryPT'.
Il renseigne la correspondance entre les clés respectives des dictionnaires 'dictOfEachPropertyUses' et 'dictOfEachPrimaryPropertyType'.
A chaque clé du dictionnaire 'dictLinkListEachPrimaryPT' correspond une clé du dictionnaire 'dictOfEachPrimaryPropertyType'.
A chaque valeur du dictionnaire 'dictLinkListEachPrimaryPT' correspond une ou plusieurs (List) valeurs du dictionnaire 'dictOfEachPropertyUses'.
dictLinkListEachPrimaryPT = {'College/University': 'College/University',
'Distribution Center': 'Distribution Center',
'Hospital': 'Hospital (General Medical & Surgical)',
'Hotel': 'Hotel',
'K-12 School': 'K-12 School',
'Laboratory': 'Laboratory',
'Large Office': 'Office',
'Low-Rise Multifamily': 'Multifamily Housing',
'Medical Office': 'Medical Office',
'Mixed Use Property': ['Social/Meeting Hall',
'Other - Entertainment/Public Assembly',
'Medical Office', 'Multifamily Housing',
'Laboratory',
'Non-Refrigerated Warehouse',
'Office',
'Retail Store',
'Parking',
'Other',
'Distribution Center',
'Supermarket/Grocery Store',
'Other - Restaurant/Bar',
'Hotel',
'Strip Mall',
'Fitness Center/Health Club/Gym',
'Other - Education',
'Restaurant',
'Residence Hall/Dormitory',
'Bank Branch',
'Other - Recreation'],
'Non-Refrigerated Warehouse': 'Non-Refrigerated Warehouse',
'Office': 'Office',
'Other': ['Police Station',
'Other - Entertainment/Public Assembly',
'Library', 'Fitness Center/Health Club/Gym',
'Courthouse',
'Other',
'Automobile Dealership',
'Museum',
'Repair Services (Vehicle, Shoe, Locksmith, etc)',
'Other - Lodging/Residential',
'Other/Specialty Hospital',
'Financial Office',
'Parking',
'Convention Center',
'Urgent Care/Clinic/Other Outpatient',
'Laboratory',
'Other - Services',
'Strip Mall',
'Wholesale Club/Supercenter',
'Other - Recreation',
'Lifestyle Center',
'Other - Public Services',
'Data Center',
'Other - Mall',
'Manufacturing/Industrial Plant',
'Social/Meeting Hall',
'Other - Education',
'Fire Station',
'Performing Arts','Residential Care Facility',
'Bank Branch',
'Food Service',
'Adult Education',
'Other - Utility',
'Movie Theater',
'Outpatient Rehabilitation/Physical Therapy',
'Personal Services (Health/Beauty, Dry Cleaning, etc)',
'Pre-school/Daycare',
'Prison/Incarceration'],
'Refrigerated Warehouse': 'Refrigerated Warehouse',
'Residence Hall': 'Residence Hall/Dormitory',
'Residence Hall/Dormitory': 'Residence Hall/Dormitory',
'Restaurant': ['Restaurant',
'Other - Restaurant/Bar'],
'Retail Store': ['Retail Store',
'Other'],
'SPS-District K-12': [],
'Self-Storage Facility': 'Self-Storage Facility',
'Senior Care Community': 'Senior Care Community',
'Small- and Mid-Sized Office': 'Office',
'Supermarket / Grocery Store': ['Supermarket/Grocery Store',
'Wholesale Club/Supercenter'],
'Supermarket/Grocery Store': 'Supermarket/Grocery Store',
'University': 'College/University',
'Warehouse': 'Non-Refrigerated Warehouse',
'Worship Facility': 'Worship Facility'}
Définition de la fonction permettant de renseigner les valeurs manquantes
des 6 colonnes selon la stratégie décrite plus haut :
# Traitement des valeurs manquantes des 6 colonnes :
# LargestPropertyUseType | LargestPropertyUseTypeGFA
# SecondLargestPropertyUseType | SecondLargestPropertyUseTypeGFA
# ThirdLargestPropertyUseType | ThirdLargestPropertyUseTypeGFA
def fillFirstSecondThirdPrimaryPropertyTypeAndGFA(row):
dictValToReturn={}
# Je renomme mes dictionnaires de référence par souci de lisibilité
dictKey = dictLinkListEachPrimaryPT
dictMean = dictOfEachPropertyUses
# String par défault si aucune valeur de disponible
ANR = 'Aucun ou Non Renseigné'
# Traitement de 'LargestPropertyUseType'
if pd.notnull(row['LargestPropertyUseType']):
dictValToReturn['LargestPropertyUseType'] = row['LargestPropertyUseType']
elif row['PrimaryPropertyType'] not in dictKey:
dictValToReturn['LargestPropertyUseType'] = ANR
elif isinstance(dictKey[row['PrimaryPropertyType']],list):
dictValToReturn['LargestPropertyUseType'] = ANR
else:
dictValToReturn['LargestPropertyUseType'] = dictKey[row['PrimaryPropertyType']]
# Traitement de 'LargestPropertyUseTypeGFA'
if pd.notnull(row['LargestPropertyUseTypeGFA']):
dictValToReturn['LargestPropertyUseTypeGFA'] = row['LargestPropertyUseTypeGFA']
elif dictValToReturn['LargestPropertyUseType'] == ANR:
dictValToReturn['LargestPropertyUseTypeGFA'] = 0
else:
dictValToReturn['LargestPropertyUseTypeGFA'] = dictMean[dictValToReturn['LargestPropertyUseType']]
# Traitement de 'SecondLargestPropertyUseType'
if pd.notnull(row['SecondLargestPropertyUseType']):
dictValToReturn['SecondLargestPropertyUseType'] = row['SecondLargestPropertyUseType']
elif row['NbListOfAllPropertyUseTypes'] != 2:
dictValToReturn['SecondLargestPropertyUseType'] = ANR
else:
secondElement = [item for item in row['ListOfAllPropertyUseTypes'] if item not in dictKey[row['PrimaryPropertyType']]][0]
if secondElement not in dictKey:
dictValToReturn['SecondLargestPropertyUseType'] = ANR
elif isinstance(dictKey[secondElement],list):
dictValToReturn['SecondLargestPropertyUseType'] = ANR
else:
dictValToReturn['SecondLargestPropertyUseType'] = dictKey[secondElement]
# Traitement de 'SecondLargestPropertyUseTypeGFA'
if pd.notnull(row['SecondLargestPropertyUseTypeGFA']):
dictValToReturn['SecondLargestPropertyUseTypeGFA'] = row['SecondLargestPropertyUseTypeGFA']
elif dictValToReturn['SecondLargestPropertyUseType'] == ANR:
dictValToReturn['SecondLargestPropertyUseTypeGFA'] = 0
else:
dictValToReturn['SecondLargestPropertyUseTypeGFA'] = dictMean[dictValToReturn['SecondLargestPropertyUseType']]
# Traitement de ThirdLargestPropertyUseType
if pd.notnull(row['ThirdLargestPropertyUseType']):
dictValToReturn['ThirdLargestPropertyUseType'] = row['ThirdLargestPropertyUseType']
else:
dictValToReturn['ThirdLargestPropertyUseType'] = ANR
# Traitement de ThirdLargestPropertyUseTypeGFA
if pd.notnull(row['ThirdLargestPropertyUseTypeGFA']):
dictValToReturn['ThirdLargestPropertyUseTypeGFA'] = row['ThirdLargestPropertyUseTypeGFA']
elif dictValToReturn['ThirdLargestPropertyUseType'] == ANR:
dictValToReturn['ThirdLargestPropertyUseTypeGFA'] = 0
else:
dictValToReturn['ThirdLargestPropertyUseTypeGFA'] = dictMean[dictValToReturn['ThirdLargestPropertyUseType']]
# On retourne le dictionnaire {dictValToReturn}
return dictValToReturn
Renseignement des 6 colonnes :
df[['LargestPropertyUseType',
'LargestPropertyUseTypeGFA',
'SecondLargestPropertyUseType',
'SecondLargestPropertyUseTypeGFA',
'ThirdLargestPropertyUseType',
'ThirdLargestPropertyUseTypeGFA']] = df.apply(lambda row: pd.Series([fillFirstSecondThirdPrimaryPropertyTypeAndGFA(row)['LargestPropertyUseType'],
fillFirstSecondThirdPrimaryPropertyTypeAndGFA(row)['LargestPropertyUseTypeGFA'],
fillFirstSecondThirdPrimaryPropertyTypeAndGFA(row)['SecondLargestPropertyUseType'],
fillFirstSecondThirdPrimaryPropertyTypeAndGFA(row)['SecondLargestPropertyUseTypeGFA'],
fillFirstSecondThirdPrimaryPropertyTypeAndGFA(row)['ThirdLargestPropertyUseType'],
fillFirstSecondThirdPrimaryPropertyTypeAndGFA(row)['ThirdLargestPropertyUseTypeGFA']]), axis=1)
La recherche de correspondance avec le champs 'OSEBuildingID' ne fonctionne malheureusement pas
sur l'ensemble des valeurs manquantes de mon jeu de données.
Lorsque la correspondance avec le champs 'OSEBuildingID' n'est pas possible,
je retourne le 'ZipCode' du bâtiment géographiquement le plus proche.
Pour déterminer le bâtiment le plus proche, je choisis le 'ZipCode' disponible du bâtiment
ayant la valeur la plus proche de la valeur absolue du produit de la 'Latitude' et de la 'Longitude'.
Cette méthode, bien qu'imprécise au niveau mondial, semble très bien fonctionner à l'échelle d'une ville,
les vérifications manuelles réalisées ont montré que les résultats obtenus étaient corrects.
J'écris la fonction me permettant d'appliquer ma stratégie
puis je renseigne ma colonne '**ZipCode**' avec cette fonction :
def fillZipCode(row):
'''Retourne le CP (CodePostal, colonne nommé ZipCode) d'un enregistrement
Si le CP est déjà renseigné alors on le retourne directement.
Sinon Si au moins une adresse identique dans le DataFrame df existe et
qu'au moins un CP est renseigné parmi ces adresses alors
on retourne ce CP.
Sinon on retourne le CP de l'enregistrement le plus proche qui a un CP renseigné.
L'enregistrement est désigné le plus proche en fonction de la valeur absolue du
produit de sa Latitude et de sa Longitude.'''
if pd.notnull(row.ZipCode):
return row.ZipCode
else:
if len(df[(df.Address == row.Address) & (df.ZipCode.notnull())]) > 0:
return df[(df.Address == row.Address) & (df.ZipCode.notnull())]['ZipCode'].iloc[0]
else:
return df.iloc[((df[df.ZipCode.notnull()]['Latitude']*df[df.ZipCode.notnull()]['Longitude'])-\
(row['Latitude']*row['Longitude'])).abs().argsort()[0]]['ZipCode']
df['ZipCode'] = df.apply(lambda row: fillZipCode(row), axis=1)
La colonne '**ZipCode**' ne contient plus de valeur **Null**,
je peux maintenant la convertir au format **int** :
df.ZipCode = df.ZipCode.astype(int)
J'effectue ici une recherche de correspondance avec la colonne 'OSEBuildingID'.
Si la recherche de correspondance ne fonctionne pas, je retourne la valeur -99.
Une fois les valeurs manquantes renseignées je nettoie cette colonne en retirant
toutes les valeurs négatives.
def fillNumOfFloors(row):
''' Renseigne le nombre d'étages d'un immeuble en fonction du champs 'OSEBuildingID' d'un autre immeuble.
Retourne le champs 'NumberofFloors' d'un immeuble ayant le même 'OSEBuildingID' que celui de <row>
et ayant le champs 'NumberofFloors' Non Null
Sinon retourne la valeur -99.'''
if pd.notnull(row.NumberofFloors):
return row.NumberofFloors
else:
if len(df[(df.OSEBuildingID == row.OSEBuildingID) & (df.NumberofFloors.notnull())]) > 0:
return df[(df.OSEBuildingID == row.OSEBuildingID) & (df.NumberofFloors.notnull())]['NumberofFloors'].iloc[0]
else:
return -99
df['NumberofFloors'] = df.apply(lambda row: fillNumOfFloors(row), axis=1)
df = df[df.NumberofFloors >= 0]
Un des objectifs de ce projet est de juger de la pertinence
de devoir calculer l'ENERGYSTARScore.
Il n'est donc pas pertinent de chercher à compléter artificiellement cette colonne,
cela biaiserait les résultats et nous éloignerait de notre objectif.
Je ne complète pas cette colonne et je filtrerai le DataFrame 'df'
sur les valeurs Non Null de 'ENERGYSTARScore' lorsque je souhaiterai étudier
l'importance de cette caractéristique.
plt.figure(figsize=(20,10))
sns.heatmap(df.isna(), cbar=False)
<AxesSubplot:>
Vérifions plus précisément s'il reste des valeurs manquantes hors colonne 'ENERGYSTARScore**' :
(df.isnull().sum()/df.shape[0]).sort_values(ascending=False)
ENERGYSTARScore 0.32669 NbListOfAllPropertyUseTypes 0.00000 ThirdLargestPropertyUseType 0.00000 BuildingType 0.00000 PrimaryPropertyType 0.00000 CouncilDistrictCode 0.00000 Neighborhood 0.00000 NumberofFloors 0.00000 PropertyGFATotal 0.00000 PropertyGFAParking 0.00000 PropertyGFABuilding 0.00000 ListOfAllPropertyUseTypes 0.00000 LargestPropertyUseType 0.00000 LargestPropertyUseTypeGFA 0.00000 SecondLargestPropertyUseType 0.00000 SecondLargestPropertyUseTypeGFA 0.00000 ThirdLargestPropertyUseTypeGFA 0.00000 SiteEnergyUse 0.00000 YearsENERGYSTARCertified 0.00000 DefaultData 0.00000 ComplianceStatus 0.00000 Outlier 0.00000 Address 0.00000 ZipCode 0.00000 Latitude 0.00000 Longitude 0.00000 ageBuilding 0.00000 Proportion_Electricity 0.00000 Proportion_NaturalGas 0.00000 Proportion_Steam 0.00000 TotalGHGEmissions 0.00000 OSEBuildingID 0.00000 dtype: float64
Les valeurs manquantes ont bien toutes été traitées.
Affichons pour terminer cette partie,
les dimensions du Dataframe '**df**' :
df.shape
(3263, 32)
Des bâtiments ont été analysés par les agents sur le terrain d'une année sur l'autre.
Ils constituent des doublons qu'il faut analyser.
Les bâtiments sont désignés par leurs id et sont renseignés dans la colonne 'OSEBuildingID'.
J'identifie 2 types de doublons :
df.duplicated(subset=['OSEBuildingID'], keep=False).value_counts()
True 3144 False 119 dtype: int64
3144 bâtiments ont un 'OSEBuildingID' présent sur l'année 2015 et l'année 2016.
Seulement 119 bâtiments ne sont présents que sur l'une des deux années 2015 ou 2016.
Pour décider quel doublon garder, je vais m'appuyer sur
les informations contenues dans les colonnes :
'DefaultData' indique si des données par défaut pour
au moins une caractéristique de la propriété ont été utilisées.
Un enregistrement ayant cette caractéristique à False sera plus précis
qu'un enregistrement qui a cette caractéristique à True.
'DefaultData' est souvent à False pour l'année 2016
lorsqu'il y a doublon sur les 2 années, mais ce n'est pas systématique.
Stratégie de conservation adoptée pour chaque bâtiment analysé :
Je filtre 'df' en affichant que les 'OSEBuildingID'
qui ont leur valeur 'DefaultData' à True.
Puis j'affiche le df trié de manière ascendante
sur les colonnes 'OSEBuildingID' et 'ageBuilding' afin
de regrouper les doublons deux à deux.
Enfin, je filtre les colonnes en affichant que les 5 qui nous intéressent ici :
df[df.OSEBuildingID.isin(df.OSEBuildingID[df.DefaultData])].sort_values\
(by=['OSEBuildingID','ageBuilding'],ascending=[True,True])\
[['OSEBuildingID','ENERGYSTARScore','ageBuilding','DefaultData','PropertyGFABuilding']].head(30)
| OSEBuildingID | ENERGYSTARScore | ageBuilding | DefaultData | PropertyGFABuilding | |
|---|---|---|---|---|---|
| 2 | 3 | 18.00000 | 46 | True | 961990 |
| 3342 | 3 | 43.00000 | 47 | False | 759392 |
| 13 | 18 | 56.00000 | 35 | True | 258352 |
| 3353 | 18 | 58.00000 | 36 | False | 258352 |
| 22 | 28 | 96.00000 | 54 | True | 56228 |
| 3362 | 28 | 95.00000 | 55 | True | 56228 |
| 27 | 33 | 20.00000 | 14 | True | 133585 |
| 3367 | 33 | 57.00000 | 15 | False | 133585 |
| 30 | 36 | 80.00000 | 66 | True | 160645 |
| 3370 | 36 | 75.00000 | 67 | True | 160645 |
| 31 | 37 | 94.00000 | 26 | True | 51582 |
| 37 | 51 | 88.00000 | 65 | True | 175565 |
| 3378 | 51 | 88.00000 | 66 | True | 175565 |
| 50 | 65 | 99.00000 | 30 | True | 71150 |
| 3390 | 65 | 98.00000 | 31 | False | 71150 |
| 64 | 95 | 24.00000 | 107 | True | 76631 |
| 3404 | 95 | 71.00000 | 108 | False | 76631 |
| 85 | 122 | 71.00000 | 24 | True | 58933 |
| 86 | 125 | 65.00000 | 58 | True | 130862 |
| 3426 | 125 | 61.00000 | 59 | True | 130862 |
| 87 | 127 | 94.00000 | 26 | True | 59955 |
| 3427 | 127 | 93.00000 | 27 | True | 59955 |
| 88 | 130 | 84.00000 | 51 | True | 93218 |
| 3428 | 130 | 86.00000 | 52 | True | 93218 |
| 92 | 137 | 88.00000 | 52 | True | 131013 |
| 3432 | 137 | 88.00000 | 53 | True | 131013 |
| 93 | 138 | 95.00000 | 56 | True | 245587 |
| 3433 | 138 | 92.00000 | 57 | False | 245587 |
| 94 | 139 | 75.00000 | 13 | True | 69138 |
| 3434 | 139 | 78.00000 | 14 | False | 69138 |
Pour sélectionner efficacement quel doublon conserver pour chaque doublon de mon DataFrame,
je crée une nouvelle colonne qui indiquera si la ligne est sélectionnée ou non.
Je renseignerai cette ligne grâce à la fonction selectDoublonOSEBuildingID ci-dessous :
def selectDoublonOSEBuildingID(row):
''' Retourne True ou False pour indiquer si l'enregistrement <row> est à conserver ou non '''
if len(df[df['OSEBuildingID']==row['OSEBuildingID']]) < 2: # Si pas de doublon pour cet OSE_ID
return True # On garde cette enregistrement
else:
secondElement = df[(df.OSEBuildingID == row['OSEBuildingID']) & (df.index != row.name)].iloc[0]
if row['PropertyGFABuilding'] != secondElement['PropertyGFABuilding']: # Si les valeurs de 'PropertyGFABuilding' sont différentes entre <row> et <secondElement>
return True # Ce n'est pas un doublon, on conserve cet enregistrement
else:
if df[df.OSEBuildingID == row['OSEBuildingID']]['ENERGYSTARScore'].notnull().iloc[0] == df[df.OSEBuildingID == row['OSEBuildingID']]['ENERGYSTARScore'].notnull().iloc[1]:
if df[df.OSEBuildingID == row['OSEBuildingID']]['DefaultData'].iloc[0] == df[df.OSEBuildingID == row['OSEBuildingID']]['DefaultData'].iloc[1]: # Si les deux OSE_ID sont à True ou les deux à False
return row['ageBuilding'] < secondElement['ageBuilding'] # On conserve l'enregistrement s'il est plus jeune que SecondElement.
else:
return not row['DefaultData'] # On conserve l'enregistrement si DefaultData est False, on le rejete sinon
else:
return pd.notnull(row['ENERGYSTARScore'])
J'indique dans une nouvelle colonne '**validationOSEBuildingID**',
les bâtiments à *conserver* ou à *supprimer* :
df['validationOSEBuildingID'] = df.apply(lambda row: selectDoublonOSEBuildingID(row), axis=1)
Il reste 1776 bâtiments.
df.validationOSEBuildingID.value_counts()
True 1776 False 1487 Name: validationOSEBuildingID, dtype: int64
Pour contrôler, nous pouvons afficher les colonnes qui sont entrées dans le processus de décision
et observer le choix final dans la colonne '**validationOSEBuildingID**' :
df[df.OSEBuildingID.isin(df.OSEBuildingID[df.DefaultData])].sort_values\
(by=['OSEBuildingID','ageBuilding'],ascending=[True,True])\
[['OSEBuildingID','ENERGYSTARScore','ageBuilding','DefaultData','PropertyGFABuilding','validationOSEBuildingID']].head(10)
| OSEBuildingID | ENERGYSTARScore | ageBuilding | DefaultData | PropertyGFABuilding | validationOSEBuildingID | |
|---|---|---|---|---|---|---|
| 2 | 3 | 18.00000 | 46 | True | 961990 | True |
| 3342 | 3 | 43.00000 | 47 | False | 759392 | True |
| 13 | 18 | 56.00000 | 35 | True | 258352 | False |
| 3353 | 18 | 58.00000 | 36 | False | 258352 | True |
| 22 | 28 | 96.00000 | 54 | True | 56228 | True |
| 3362 | 28 | 95.00000 | 55 | True | 56228 | False |
| 27 | 33 | 20.00000 | 14 | True | 133585 | False |
| 3367 | 33 | 57.00000 | 15 | False | 133585 | True |
| 30 | 36 | 80.00000 | 66 | True | 160645 | True |
| 3370 | 36 | 75.00000 | 67 | True | 160645 | False |
Je filtre mon DataFrame '**df**' avec les batiments séléctionnés :
df = df[df.validationOSEBuildingID]
Je supprime les deux colonnes '**OSEBuildingID**' et '**validationOSEBuildingID**' de mon DataFrame :
df.drop(['OSEBuildingID','validationOSEBuildingID'], axis=1, inplace=True)
Les outliers (ou valeurs aberrantes) sont généralement définis
comme toutes valeurs extrêmes, supérieures ou inférieures à X fois
l’écart interquartile IQR.
Généralement X vaut '1,5'.
L’écart interquartile IQR correspond à la différence entre la valeur
du 3ème quartile et la valeur du 1er quartile.
Une donnée identifiée comme outlier ne permet pas,
à priori, de juger de la pertinence de la présence d'une valeur dans l'échantillon.
Une analyse métier doit être faite pour distinguer les valeurs
légitimes qu'il faut conserver des valeurs abérrantes issues d'une
erreur (humaine, de convertion d'unité, etc.) et qui doivent être supprimées.
Ici je vais commencer par visualiser les outliers de nos différentes variables quantitatives.
Ensuite je filtrerai les valeurs identifiées comme aberrantes en utilisant la distance de Cook.
Cependant, je commencerai par éliminer quelques Outliers
sur la base d'une analyse métier que je décrierai ci-dessous.
Dimension du DataFrame '**df**' avant traitement des Outliers :
df.shape
(1776, 31)
D'après Wikipédia, le plus haut immeuble de Seattle à 76 étages.
Je supprime les enregistrements d'immeubles ayant plus de 76 étages :
df = df[df.NumberofFloors <= 76]
print('Nombre d\'enregistrements restants dans \'df\' :',df.shape[0])
Nombre d'enregistrements restants dans 'df' : 1775
Je supprime également les enregistrements qui ont
leur '**LargestPropertyUseType**' plus petit ou égal à 0 :
df = df[df.LargestPropertyUseTypeGFA > 0]
print('Nombre d\'enregistrements restants dans \'df\' :',df.shape[0])
Nombre d'enregistrements restants dans 'df' : 1754
Je supprime les bâtiments qui n'ont qu'un seul type de propriété
et dont la surface totale du bâtiment est supérieure à au moins 2 fois
la superficie de cette propriété :
df = df[(df.NbListOfAllPropertyUseTypes != 1) | (df.PropertyGFABuilding <= 2*df.LargestPropertyUseTypeGFA)]
print('Nombre d\'enregistrements restants dans \'df\' :',df.shape[0])
Nombre d'enregistrements restants dans 'df' : 1739
Il me parrait aberrant d'avoir des enregistrements avec
des variables 'PropertyGFATotal',
qui correspondent à la superficie totale de la propriété,
être inférieures à la variable 'LargestPropertyUseTypeGFA',
qui elle, n'est qu'une composante de la surface de cette propriété.
Je supprime donc ces valeurs aberrantes :
df = df[df.PropertyGFATotal >= df.LargestPropertyUseTypeGFA]
print('Nombre d\'enregistrements restants dans \'df\' :',df.shape[0])
Nombre d'enregistrements restants dans 'df' : 1497
Et enfin, je supprime toutes les valeurs négatives
des colonnes renseignant sur des surfaces :
df = df[(df.filter(regex='GFA', axis=1) >= 0).all(1)]
print('Nombre d\'enregistrements restants dans \'df\' :',df.shape[0])
Nombre d'enregistrements restants dans 'df' : 1495
Je mets à jour ma liste des variables continues,
présente dans la List Ordre_Distance :
Ordre_Distance = [ele for ele in Ordre_Distance if ele in df.columns]
Commençons par afficher sous forme de BoxPlot,
la distribution de nos données de nos variables continues :
Nous attarderons ici sur les Outliers identifiés dans Seaborn et
affichés sous forme de losanges et définis selon
le calcul (+ ou -)1.5 l'écart interquartile.*
colToAnalyse = Ordre_Distance
for col in colToAnalyse:
fig = plt.figure(figsize=(15,5))
fig.suptitle(col, fontsize=30)
chart = sns.boxplot(
data=df,
x=col,
palette='Set2',
showfliers = True
)
La distance de Cook est une mesure de l’influence d’un point de données.
C’est un moyen de trouver des valeurs aberrantes influentes dans un ensemble
de variables prédictives lors de l’exécution d’une analyse des moindres carrés.
Concrètement, nous calculons une Régression Linéaire sur notre modèle,
puis nous supprimons 1 à 1 les points de notre modèle en recalculant
à chaque fois une Régression Linéaire.
Si le score de la Régression Linéaire change drastiquement,
alors ce point est considéré comme très influent.
Ces points influents sont des valeurs extrêmes qui peuvent être des valeurs aberrantes.
Dans ce projet, je décide de filtrer toutes valeurs extrêmes identifiées par la **Distance de Cook**..
J'applique la méthode sur mes variables continues.
J'utilise comme Target, pour la Regression Linéaire, la variable 'TotalGHGEmissions'
Le graphique ci-dessous affiche l'influence des points
en fonction des index des points de notre jeu de données.
On observe 1 point qui a particulièrement d'influence vers l'index 500,
ainsi que quelques autres qui se démarquent également mais dans une moindre mesure.
Le graphique nous informe que 4.68 % des points sont au dessus
de la limite qui définit les valeurs extrêmes qui seront supprimées.
X = df[[ele for ele in Ordre_Distance if ele not in ['TotalGHGEmissions','SiteEnergyUse','ENERGYSTARScore']]]
y = df.TotalGHGEmissions
visualizer = CooksDistance()
visualizer.fit(X,y)
visualizer.show()
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.
warnings.warn('From version 0.24, get_params will raise an '
<AxesSubplot:title={'center':"Cook's Distance Outlier Detection"}, xlabel='instance index', ylabel='influence (I)'>
Valeur du seuil :
Le seuil est calculé selon la formule 4/n où n représente le nombre d'échantillon
visualizer.influence_threshold_
0.0026755852842809363
J'indique pour chaque index de mon jeu de données,
ceux qui sont définis avec une faible influence :
idxLowInfluence = (visualizer.distance_ <= visualizer.influence_threshold_)
df = df[df.index.isin(X[idxLowInfluence].index)]
print('Nombre d\'enregistrements restants dans \'df\' :',df.shape[0])
Nombre d'enregistrements restants dans 'df' : 1425
Observons de nouveau la distribution des données de nos variables continues.
Les Outliers identifiés par Seaborn sont toujours présents.
Cependant les points les plus extrêmes ont été retirés,
précisément ceux qui étaient les plus influents et potentiellement nuisibles
à la qualité de prédiction des modèles de Machine Learning.
for col in colToAnalyse:
fig = plt.figure(figsize=(15,5))
fig.suptitle(col, fontsize=30)
chart = sns.boxplot(
data=df,
x=col,
palette='Set2',
showfliers = True
)
**Les données catégorielles** :
Afin que nos données puissent être comprises par les algorithmes de Machines Learning,
les données catégorielles doivent être converties en données numériques.
Mes données qui n’ont pas de notion de distance mais ont cependant
une notion d’ordre seront encodés avec l’encodeur 'Label Encoder' ou 'Ordinal Encoder'.
'Ordinal Encoder' nous permet de gérer des données en plusieurs dimensions,
et nous permet surtout de définir l’ordre d’encodage de nos catégories.
'Label Encoder' est normalement prévu pour encoder les Labels, soit des données en 1 seule dimension.
J’utiliserai les deux méthodes dans le cadre de ce projet.
Mes données qui n’ont ni de notion d’ordre ni de notion de distance seront encodés avec un 'One-Hot-Encoder'.
A chaque catégorie sera associée une colonne qui lui sera propre, remplie uniquement de 0 ou de 1.
De plus, dans le cas où une variable aurait beaucoup de modalités
et où seulement certaines modalités regrouperaient à elles seules une part
importante des échantillons, alors je filtrerai le résultat de la transformation 'One-Hot-Encoder'
pour ne garder que ces modalités majoritaires.
Dans ce projet, et dans certains cas en fonction de la cardinalité des variables,
je choisirai de ne conserver que les modalités qui représentent au moins 80% des échantillons.
Par exemple, si une variable contient une liste de 100 fruits et légumes et que les fruits ‘pommes’
et ‘orange’ représentent à eux seul 80% des échantillons, alors je ne conserverai que ces deux modalités.
Pour mes variables contenant des List de valeurs, j’utiliserai l’encodeur 'MultiLabelEncodeur'.
Il reprend le principe du 'One-Hot-Encoder' mais ici plusieurs colonnes
peuvent-être à 1 là où une seule colonne peut-être à 1 avec l'encodeur 'One-Hot-Encoder'.
Les données continues :
Les données continues (avec une notion d’ordre et de distance),
doivent, dans la plus part des cas, être transformées
si nous voulons les optimiser avant de les donner à nos algorithmes de Machine Learning.
En effet, pour que des variables mesurées à différentes échelles,
aient des valeurs comparables, elles doivent préalablement être standardisées.
La standardisation consiste à transformer nos données de tel sorte
à ce que chaque variable ait une moyenne égale à 0 et un écart-type égale à 1.
Il faut pour cela soustraire chaque valeur à la moyenne initiale de notre variable
et diviser le total par l’écart type initial de notre variable.
Les données ainsi obtenues sont beaucoup plus simples à utiliser pour
la plupart des modèles statistiques.
Je procèderai à différents types de transformation sur mes variables,
comme la Standardisation avec le transformeur 'StandardScaler'
ou le passage au Log avec le transformeur 'BoxCox'.
listVarCatCont = [Ordre_Distance,NoDistance_Ordre,NoDistance_NoOrdre]
listVarCatCont
[['ageBuilding', 'NumberofFloors', 'PropertyGFATotal', 'PropertyGFAParking', 'PropertyGFABuilding', 'LargestPropertyUseTypeGFA', 'SecondLargestPropertyUseTypeGFA', 'ThirdLargestPropertyUseTypeGFA', 'ENERGYSTARScore', 'Latitude', 'Longitude', 'Proportion_Electricity', 'Proportion_NaturalGas', 'Proportion_Steam', 'TotalGHGEmissions', 'SiteEnergyUse', 'NbListOfAllPropertyUseTypes'], ['Outlier', 'DefaultData'], ['BuildingType', 'PrimaryPropertyType', 'CouncilDistrictCode', 'Neighborhood', 'ComplianceStatus', 'City Council Districts', 'Address', 'ZipCode', 'YearsENERGYSTARCertified', 'ListOfAllPropertyUseTypes', 'LargestPropertyUseType', 'SecondLargestPropertyUseType', 'ThirdLargestPropertyUseType']]
Certaines de ces features ont été supprimées lors de mon analyse.
Je mets à jour cette liste en ne conservant que les features encore existantes :
listVarCatCont = [[j for j in i if j in df.columns] for i in listVarCatCont]
listVarCatCont
[['ageBuilding', 'NumberofFloors', 'PropertyGFATotal', 'PropertyGFAParking', 'PropertyGFABuilding', 'LargestPropertyUseTypeGFA', 'SecondLargestPropertyUseTypeGFA', 'ThirdLargestPropertyUseTypeGFA', 'ENERGYSTARScore', 'Latitude', 'Longitude', 'Proportion_Electricity', 'Proportion_NaturalGas', 'Proportion_Steam', 'TotalGHGEmissions', 'SiteEnergyUse', 'NbListOfAllPropertyUseTypes'], ['Outlier', 'DefaultData'], ['BuildingType', 'PrimaryPropertyType', 'CouncilDistrictCode', 'Neighborhood', 'ComplianceStatus', 'Address', 'ZipCode', 'YearsENERGYSTARCertified', 'ListOfAllPropertyUseTypes', 'LargestPropertyUseType', 'SecondLargestPropertyUseType', 'ThirdLargestPropertyUseType']]
[[j for j in i if j in df.columns] for i in [Ordre_Distance,NoDistance_Ordre,NoDistance_NoOrdre]]
[['ageBuilding', 'NumberofFloors', 'PropertyGFATotal', 'PropertyGFAParking', 'PropertyGFABuilding', 'LargestPropertyUseTypeGFA', 'SecondLargestPropertyUseTypeGFA', 'ThirdLargestPropertyUseTypeGFA', 'ENERGYSTARScore', 'Latitude', 'Longitude', 'Proportion_Electricity', 'Proportion_NaturalGas', 'Proportion_Steam', 'TotalGHGEmissions', 'SiteEnergyUse', 'NbListOfAllPropertyUseTypes'], ['Outlier', 'DefaultData'], ['BuildingType', 'PrimaryPropertyType', 'CouncilDistrictCode', 'Neighborhood', 'ComplianceStatus', 'Address', 'ZipCode', 'YearsENERGYSTARCertified', 'ListOfAllPropertyUseTypes', 'LargestPropertyUseType', 'SecondLargestPropertyUseType', 'ThirdLargestPropertyUseType']]
J'utilise un dictionnaire, pour plus de lisibilité à l'utilisation
dictVarCatCont = {'Ordre_Distance': listVarCatCont[0],
'NoDistance_Ordre':listVarCatCont[1],
'NoDistance_NoOrdre':listVarCatCont[2]}
Affichage des variables à encoder avec la répartition de leurs modalités :
dictVarCatCont['NoDistance_Ordre']
['Outlier', 'DefaultData']
df[dictVarCatCont['NoDistance_Ordre'][0]].value_counts()
No Outlier 1402 High Outlier 12 Low Outlier 11 Name: Outlier, dtype: int64
df[dictVarCatCont['NoDistance_Ordre'][1]].value_counts()
False 1353 True 72 Name: DefaultData, dtype: int64
J'utilise l'encodeur OrdinalEncoder pour tranformer la feature 'Outlier'
car je souhaite classer manuellement l'ordre des catégories.
J'utiliserai l'encodeur LabelEncoder pour transformer la feature 'DefaultData'.
X = df['Outlier']
X = X.values.reshape(-1,1)
oe = OrdinalEncoder(categories=[['No Outlier','Low Outlier','High Outlier']], # Je définis l'ordre de mes catégories
dtype=int) # Les données transformées seront au format Integer
df['Outlier'] = oe.fit_transform(X)
Je vérifie l'ordre de mes catégories :
oe.categories_
[array(['No Outlier', 'Low Outlier', 'High Outlier'], dtype=object)]
X = df['DefaultData']
le = LabelEncoder()
df['DefaultData'] = le.fit_transform(X)
df[dictVarCatCont['NoDistance_Ordre']]
| Outlier | DefaultData | |
|---|---|---|
| 0 | 0 | 0 |
| 1 | 0 | 0 |
| 5 | 0 | 0 |
| 6 | 0 | 0 |
| 7 | 0 | 0 |
| ... | ... | ... |
| 6711 | 0 | 1 |
| 6712 | 0 | 0 |
| 6713 | 0 | 0 |
| 6714 | 0 | 0 |
| 6715 | 0 | 0 |
1425 rows × 2 columns
Liste des variables concernées :
dictVarCatCont['NoDistance_NoOrdre']
['BuildingType', 'PrimaryPropertyType', 'CouncilDistrictCode', 'Neighborhood', 'ComplianceStatus', 'Address', 'ZipCode', 'YearsENERGYSTARCertified', 'ListOfAllPropertyUseTypes', 'LargestPropertyUseType', 'SecondLargestPropertyUseType', 'ThirdLargestPropertyUseType']
Aperçu des variables concernées :
df[dictVarCatCont['NoDistance_NoOrdre']].head().T
| 0 | 1 | 5 | 6 | 7 | |
|---|---|---|---|---|---|
| BuildingType | NonResidential | NonResidential | Nonresidential COS | NonResidential | NonResidential |
| PrimaryPropertyType | Hotel | Hotel | Other | Hotel | Other |
| CouncilDistrictCode | 7 | 7 | 7 | 7 | 7 |
| Neighborhood | DOWNTOWN | DOWNTOWN | DOWNTOWN | DOWNTOWN | DOWNTOWN |
| ComplianceStatus | Compliant | Compliant | Compliant | Compliant | Compliant |
| Address | 405 OLIVE WAY | 724 PINE ST | 810 VIRGINIA ST | 1619 9TH AVE | 901 PINE ST |
| ZipCode | 98101 | 98101 | 98101 | 98101 | 98101 |
| YearsENERGYSTARCertified | [] | [] | [] | [] | [] |
| ListOfAllPropertyUseTypes | [Hotel] | [Hotel, Parking, Restaurant] | [Police Station] | [Hotel] | [Other - Entertainment/Public Assembly] |
| LargestPropertyUseType | Hotel | Hotel | Police Station | Hotel | Other - Entertainment/Public Assembly |
| SecondLargestPropertyUseType | Aucun ou Non Renseigné | Parking | Aucun ou Non Renseigné | Aucun ou Non Renseigné | Aucun ou Non Renseigné |
| ThirdLargestPropertyUseType | Aucun ou Non Renseigné | Restaurant | Aucun ou Non Renseigné | Aucun ou Non Renseigné | Aucun ou Non Renseigné |
Cet encodeur sert normalement à encoder un Label, soit une seule colonne.
J'opère donc séparément pour chaque colonne.
J'affiche la liste des différentes valeurs présentes dans les listes
contenues dans la colonne '**YearsENERGYSTARCertified**' :
print('Liste des {} élements présents dans la colonne \'YearsENERGYSTARCertified\': \n{}'\
.format(len({j for i in df.YearsENERGYSTARCertified for j in i}),
{j for i in df.YearsENERGYSTARCertified for j in i}))
Liste des 13 élements présents dans la colonne 'YearsENERGYSTARCertified':
{2016, 2017, 2000, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015}
Aperçu de la colonne '**YearsENERGYSTARCertified**' filtrée sans valeur Null :
df[df['YearsENERGYSTARCertified'].str.len() != 0]['YearsENERGYSTARCertified'].head(10)
36 [2016] 43 [2016] 53 [2014] 136 [2016] 208 [2016, 2015, 2014, 2013, 2012, 2011, 2010, 200... 210 [2010, 2009, 2007] 212 [2010, 2007] 220 [2015, 2014, 2012, 2011, 2009] 221 [2012, 2009, 2008] 224 [2016, 2015] Name: YearsENERGYSTARCertified, dtype: object
Encodage et conservation des seules modalités qui représentent 80% des échantillons :
mlb = MultiLabelBinarizer()
df[['YearsENERGYSTARCertified_{}'.format(i) for i in mlb.classes_]] = \
pd.DataFrame(mlb.fit_transform(df.YearsENERGYSTARCertified),
columns=mlb.classes_, # Les classes sont utilisées pour créer les nouvelles colonnes
index=df.index) # On reprend les index du DataFrame 'df' pour l'alignement des valeurs
# Suppression de la colonne d'origine devenue inutile
# Suppression des colonnes les moins renseignées (qui regroupent moins de 20% des valeurs encodées)
df.drop(['YearsENERGYSTARCertified']+\
['YearsENERGYSTARCertified_{}'.format(i) for \
i in list((pd.DataFrame(mlb.fit_transform(df.YearsENERGYSTARCertified),
columns=mlb.classes_,
index=df.index).sum().sort_values(ascending=False)/\
pd.DataFrame(mlb.fit_transform(df.YearsENERGYSTARCertified),
columns=mlb.classes_,
index=df.index).sum().sort_values(ascending=False).sum()).cumsum()\
[(pd.DataFrame(mlb.fit_transform(df.YearsENERGYSTARCertified),
columns=mlb.classes_,
index=df.index).sum().sort_values(ascending=False)/\
pd.DataFrame(mlb.fit_transform(df.YearsENERGYSTARCertified),
columns=mlb.classes_,
index=df.index).sum().sort_values(ascending=False).sum()).cumsum() > 0.80].index)],
axis=1,
inplace=True)
Aperçu du DataFrame '**df**' sans valeur null dans l'ensemble des colonnes transformées :
df[df[df.columns[df.columns.str.contains('YearsENERGYSTARCertified')]].any(axis='columns')].head(10)
| BuildingType | PrimaryPropertyType | CouncilDistrictCode | Neighborhood | NumberofFloors | PropertyGFATotal | PropertyGFAParking | PropertyGFABuilding | ListOfAllPropertyUseTypes | LargestPropertyUseType | LargestPropertyUseTypeGFA | SecondLargestPropertyUseType | SecondLargestPropertyUseTypeGFA | ThirdLargestPropertyUseType | ThirdLargestPropertyUseTypeGFA | ENERGYSTARScore | DefaultData | ComplianceStatus | Outlier | Address | ZipCode | Latitude | Longitude | ageBuilding | Proportion_Electricity | Proportion_NaturalGas | Proportion_Steam | TotalGHGEmissions | SiteEnergyUse | NbListOfAllPropertyUseTypes | YearsENERGYSTARCertified_2008 | YearsENERGYSTARCertified_2009 | YearsENERGYSTARCertified_2012 | YearsENERGYSTARCertified_2013 | YearsENERGYSTARCertified_2014 | YearsENERGYSTARCertified_2015 | YearsENERGYSTARCertified_2016 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36 | SPS-District K-12 | K-12 School | 7 | MAGNOLIA / QUEEN ANNE | 2.00000 | 54986 | 0 | 54986 | [K-12 School] | K-12 School | 54986.00000 | Aucun ou Non Renseigné | 0.00000 | Aucun ou Non Renseigné | 0.00000 | 91.00000 | 0 | Compliant | 0 | 4017 26TH AVE W | 98199 | 47.65726 | -122.39065 | 25 | 0.77429 | 0.22571 | 0.00000 | 28.78000 | 1655612.00000 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 43 | NonResidential | Large Office | 7 | DOWNTOWN | 5.00000 | 168115 | 0 | 168115 | [Data Center, Office, Retail Store] | Office | 103501.00000 | Retail Store | 65676.00000 | Data Center | 354.00000 | 79.00000 | 0 | Compliant | 0 | 1513 5TH AVE | 98101 | 47.61104 | -122.33635 | 42 | 1.00000 | 0.00000 | 0.00000 | 64.23000 | 9213841.00000 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 53 | NonResidential | Hotel | 7 | LAKE UNION | 7.00000 | 150453 | 34735 | 115718 | [Hotel, Restaurant] | Hotel | 107547.00000 | Restaurant | 3000.00000 | Aucun ou Non Renseigné | 0.00000 | 83.00000 | 0 | Compliant | 0 | 211 DEXTER AVE N | 98109 | 47.62031 | -122.34280 | 15 | 0.53347 | 0.46653 | 0.00000 | 191.58000 | 6722886.00000 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 136 | SPS-District K-12 | K-12 School | 1 | DELRIDGE | 2.00000 | 367884 | 0 | 367884 | [K-12 School] | K-12 School | 367884.00000 | Aucun ou Non Renseigné | 0.00000 | Aucun ou Non Renseigné | 0.00000 | 93.00000 | 0 | Compliant | 0 | 2500 SW THISTLE ST | 98106 | 47.52967 | -122.36666 | 5 | 0.67159 | 0.32841 | 0.00000 | 271.32000 | 12263687.00000 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 208 | NonResidential | Large Office | 7 | DOWNTOWN | 23.00000 | 747747 | 205076 | 542671 | [Office, Parking, Restaurant] | Office | 542461.00000 | Parking | 219045.00000 | Restaurant | 9337.00000 | 88.00000 | 0 | Compliant | 0 | 1700 7TH AVE | 98101 | 47.61375 | -122.33508 | 15 | 0.89835 | 0.10165 | 0.00000 | 374.82000 | 32141070.00000 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 210 | NonResidential | Large Office | 7 | DOWNTOWN | 17.00000 | 204790 | 30000 | 174790 | [Office, Other - Recreation, Parking, Retail S... | Office | 142034.00000 | Other - Recreation | 17070.00000 | Retail Store | 14666.00000 | 92.00000 | 0 | Compliant | 0 | 1809 7TH AVE | 98101 | 47.61391 | -122.33581 | 85 | 0.87857 | 0.12143 | 0.00000 | 94.89000 | 7546235.00000 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 220 | NonResidential | Large Office | 7 | DOWNTOWN | 15.00000 | 388934 | 0 | 388934 | [Office] | Office | 388934.00000 | Aucun ou Non Renseigné | 0.00000 | Aucun ou Non Renseigné | 0.00000 | 84.00000 | 0 | Compliant | 0 | 710 2ND AVE | 98104 | 47.60350 | -122.33227 | 93 | 0.95287 | 0.00000 | 0.04713 | 208.81000 | 20309614.00000 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 |
| 221 | NonResidential | Large Office | 7 | DOWNTOWN | 22.00000 | 384302 | 0 | 384302 | [Convenience Store without Gas Station, Food S... | Office | 348969.00000 | Convenience Store without Gas Station | 4181.00000 | Food Service | 1437.00000 | 98.00000 | 0 | Compliant | 0 | 821 2ND AVE | 98104 | 47.60403 | -122.33431 | 86 | 1.00000 | 0.00000 | 0.00000 | 68.75000 | 9861998.00000 | 3 | 1 | 1 | 1 | 0 | 0 | 0 | 0 |
| 224 | NonResidential | Large Office | 7 | DOWNTOWN | 41.00000 | 589921 | 129432 | 460489 | [Office, Parking, Pre-school/Daycare] | Office | 485296.00000 | Parking | 92787.00000 | Pre-school/Daycare | 10290.00000 | 82.00000 | 0 | Compliant | 0 | 1000 2ND AVE | 98104 | 47.60533 | -122.33504 | 29 | 1.00000 | 0.00000 | 0.00000 | 182.71000 | 26208138.00000 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 226 | NonResidential | Large Office | 7 | DOWNTOWN | 8.00000 | 157941 | 62993 | 94948 | [Office, Parking] | Office | 84838.00000 | Parking | 15401.00000 | Aucun ou Non Renseigné | 0.00000 | 89.00000 | 0 | Compliant | 0 | 1101 2ND AVE | 98101 | 47.60571 | -122.33639 | 47 | 1.00000 | 0.00000 | 0.00000 | 22.01000 | 3157579.00000 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
J'affiche la liste des différentes valeurs présentes dans les listes
contenues dans la colonne '**ListOfAllPropertyUseTypes**' :
print('Liste des {} élements présents dans la colonne \'ListOfAllPropertyUseTypes\':'\
.format(len({j for i in df.ListOfAllPropertyUseTypes for j in i})))
Liste des 62 élements présents dans la colonne 'ListOfAllPropertyUseTypes':
{j for i in df.ListOfAllPropertyUseTypes for j in i}
{'Adult Education',
'Automobile Dealership',
'Bank Branch',
'Bar/Nightclub',
'College/University',
'Convenience Store without Gas Station',
'Courthouse',
'Data Center',
'Distribution Center',
'Enclosed Mall',
'Fast Food Restaurant',
'Financial Office',
'Fitness Center/Health Club/Gym',
'Food Sales',
'Food Service',
'Hotel',
'K-12 School',
'Laboratory',
'Library',
'Lifestyle Center',
'Manufacturing/Industrial Plant',
'Medical Office',
'Movie Theater',
'Multifamily Housing',
'Museum',
'Non-Refrigerated Warehouse',
'Office',
'Other',
'Other - Education',
'Other - Entertainment/Public Assembly',
'Other - Lodging/Residential',
'Other - Mall',
'Other - Public Services',
'Other - Recreation',
'Other - Restaurant/Bar',
'Other - Services',
'Other - Technology/Science',
'Other - Utility',
'Other/Specialty Hospital',
'Outpatient Rehabilitation/Physical Therapy',
'Parking',
'Performing Arts',
'Personal Services (Health/Beauty, Dry Cleaning, etc)',
'Police Station',
'Pre-school/Daycare',
'Prison/Incarceration',
'Refrigerated Warehouse',
'Repair Services (Vehicle, Shoe, Locksmith, etc)',
'Residence Hall/Dormitory',
'Residential Care Facility',
'Restaurant',
'Retail Store',
'Self-Storage Facility',
'Senior Care Community',
'Social/Meeting Hall',
'Strip Mall',
'Supermarket/Grocery Store',
'Swimming Pool',
'Urgent Care/Clinic/Other Outpatient',
'Vocational School',
'Wholesale Club/Supercenter',
'Worship Facility'}
Encodage et conservation des seules modalités qui représentent 83% des échantillons :
Je choisis volontairement 83% pour conserver la modalité 'DataCenter' que je juge, à priori, intéressante.
mlb = MultiLabelBinarizer()
df[['ListOfAllPropertyUseTypes_{}'.format(i) for i in mlb.classes_]] = \
pd.DataFrame(mlb.fit_transform(df.ListOfAllPropertyUseTypes),
columns=mlb.classes_,
index=df.index)
df.drop(['ListOfAllPropertyUseTypes']+\
['ListOfAllPropertyUseTypes_{}'.format(i) for \
i in list((pd.DataFrame(mlb.fit_transform(df.ListOfAllPropertyUseTypes),
columns=mlb.classes_,
index=df.index).sum().sort_values(ascending=False)/\
pd.DataFrame(mlb.fit_transform(df.ListOfAllPropertyUseTypes),
columns=mlb.classes_,
index=df.index).sum().sort_values(ascending=False).sum()).cumsum()\
[(pd.DataFrame(mlb.fit_transform(df.ListOfAllPropertyUseTypes),
columns=mlb.classes_,
index=df.index).sum().sort_values(ascending=False)/\
pd.DataFrame(mlb.fit_transform(df.ListOfAllPropertyUseTypes),
columns=mlb.classes_,
index=df.index).sum().sort_values(ascending=False).sum()).cumsum() > 0.83].index)],
axis=1,
inplace=True)
Aperçu des colonnes encodées, filtrées sans valeurs Null :
df[df.filter(regex='^ListOfAll', axis=1).any(axis='columns')].filter(regex='^ListOfAll', axis=1).head().T
| 0 | 1 | 6 | 8 | 12 | |
|---|---|---|---|---|---|
| ListOfAllPropertyUseTypes_Data Center | 0 | 0 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Distribution Center | 0 | 0 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Hotel | 1 | 1 | 1 | 1 | 1 |
| ListOfAllPropertyUseTypes_K-12 School | 0 | 0 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Medical Office | 0 | 0 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Multifamily Housing | 0 | 0 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Non-Refrigerated Warehouse | 0 | 0 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Office | 0 | 0 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Other | 0 | 0 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Parking | 0 | 1 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Restaurant | 0 | 1 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Retail Store | 0 | 0 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Supermarket/Grocery Store | 0 | 0 | 0 | 0 | 0 |
| ListOfAllPropertyUseTypes_Worship Facility | 0 | 0 | 0 | 0 | 0 |
Je mets à jour ma liste des variables sans notion de distance ni d'ordre
en retirant les deux variables 'YearsENERGYSTARCertified' et 'ListOfAllPropertyUseTypes'
dictVarCatCont['NoDistance_NoOrdre'] = [i for i in dictVarCatCont['NoDistance_NoOrdre'] \
if i not in ['YearsENERGYSTARCertified','ListOfAllPropertyUseTypes']]
dictVarCatCont['NoDistance_NoOrdre']
['BuildingType', 'PrimaryPropertyType', 'CouncilDistrictCode', 'Neighborhood', 'ComplianceStatus', 'Address', 'ZipCode', 'LargestPropertyUseType', 'SecondLargestPropertyUseType', 'ThirdLargestPropertyUseType']
Cardinalité des variables '**NoDistance_NoOrdre**' à traiter :
df[dictVarCatCont['NoDistance_NoOrdre']].nunique().sort_values()
ComplianceStatus 3 BuildingType 4 CouncilDistrictCode 7 Neighborhood 13 PrimaryPropertyType 25 ZipCode 30 ThirdLargestPropertyUseType 38 SecondLargestPropertyUseType 43 LargestPropertyUseType 54 Address 1364 dtype: int64
Je traiterai les variables par lot suivant leur cardinalité.
Je traiterai dans l'odre :
J'écris une fonction qui me permet d'automatiser la transformation
en One-Hot-Encoder d'une ou plusieurs variables.
Cette fonction reçoit en entrée un DataFrame
avec une List de colonne à transformer.
La fonction retourne le DataFrame avec les colonnes transmises transformées
et avec les colonnes d'origine retirées.
Cette fonction permet également :
def reducFeaturesAndOneHotEncode(df,col,percentFeatures=0.8,delLastModIfNoDummyValue = False):
''' Cette fonction travaille sur la colonne <col> du DataFrame <df>.
L'objectif est d'appliquer un OneHotEncoder (OHE) sur la colonne <col>
en conservant uniquement les features qui représente jusqu'à
<percentFeatures>% (par défaut 80% ) des modalités de la colonnes <col>
Une fois la transformation OHE appliquée, la fonction retourne le df modifié.
Erreur connue :
- Erreur si traitement d'une colonne avec deux modalités: [True,False] avec dtype != bool
- Solution: Convertir la colonne en bool avant d'appeler la fonction ==> df[col] = df[col].astype(bool)
'''
from sklearn.preprocessing import OneHotEncoder
data = df.copy()
if isinstance(col,str):
col=[col] # Si <col> est une string, je convertis en une List d'un seul élement
for i in col:
if data[i].dtype == object: # Choix de la dummyValue en fonction du type de données dans la colonne traitée
dummyValue = 'A_Supprimer'
else:
dummyValue = 99999
lastIndexCumSum = data[i].value_counts(normalize=True).cumsum().index[-1]# Enregistrement de la dernière modalité du value_counts(normalize=True).cumsum() pour supprimer la dernière colonne du OHE si besoin
if (data[i].value_counts(normalize=True).cumsum() > percentFeatures).all(): # Si toutes les modalités sont au dessus du seuil, on selectionne toutes les modalités sauf la plus représentée
print(i,': Attention : percentFeatures =',percentFeatures,'trop bas. Aucune colonne conservée.')
print(i,': Correction automatique : Colonne ayant la plus haute modalité conservée :',(data[i].value_counts(normalize=True).cumsum().index[0]))
if data[i].dtype == bool: # Traitement particulier pour les pd.Series de type bool, mappage necessaire des valeurs
data.loc[(data[i].value_counts(normalize=True).cumsum()[1:] >
percentFeatures).append(pd.Series(not (data[i].value_counts(normalize=True).cumsum() >
percentFeatures)[0],index=[(data[i].value_counts(normalize=True).cumsum() >
percentFeatures).index[0]]))[data[i].map({True:1,False:0})].values,i] = dummyValue
else:
data.loc[(data[i].value_counts(normalize=True).cumsum()[1:] >
percentFeatures).append(pd.Series(not (data[i].value_counts(normalize=True).cumsum() >
percentFeatures)[0],index=[(data[i].value_counts(normalize=True).cumsum() >
percentFeatures).index[0]]))[data[i]].values,i] = dummyValue
else: # Sinon on applique la <dummyValue> aux modalités les moins représentées et sous le seuil <percentFeatures>
if data[i].dtype == bool: # Traitement particulier pour les pd.Series de type bool, mappage necessaire des valeurs
data.loc[(data[i].value_counts(normalize=True).cumsum() >
percentFeatures)[data[i].map({True:1,False:0})].values,i] = dummyValue
else:
data.loc[(data[i].value_counts(normalize=True).cumsum() >
percentFeatures)[data[i]].values,i] = dummyValue
ohe = OneHotEncoder(dtype=int)
data[ohe.get_feature_names([i])] = pd.DataFrame(ohe.fit_transform(data[i].values.reshape(-1, 1)).toarray(),
columns=ohe.categories_[0],
index=data.index)
if i+'_'+str(dummyValue) in data.columns:
data.drop([i,i+'_'+str(dummyValue)], axis=1, inplace=True) # On efface la colonne d'origine + la colonne qui regroupe les valeurs peu représentées
print(i,': Colonne effacée ayant le moins de modalités :', dummyValue)
elif delLastModIfNoDummyValue:
data.drop([i,i+'_'+str(lastIndexCumSum)], axis=1, inplace=True) # On efface la colonne d'origine + la colonne ayant le moins de modalités
print(i,': Colonne effacée ayant le moins de modalités :', lastIndexCumSum)
else:
data.drop(i, axis=1, inplace=True) # On efface la colonne d'origine
print(i,': Aucune colonne effacée.')
return data
Encodage en **OneHotEncoder** des variables :
Affichons également la fréquence relative
des modalités des variables à encoder :
print('################################')
for i in ['ComplianceStatus',
'BuildingType',
'CouncilDistrictCode',
'Neighborhood']:
print('################################')
print(i)
print(df[i].value_counts(normalize=True))
print('################################')
print('################################')
################################ ################################ ComplianceStatus Compliant 0.99579 Error - Correct Default Data 0.00351 Non-Compliant 0.00070 Name: ComplianceStatus, dtype: float64 ################################ BuildingType NonResidential 0.89754 Nonresidential COS 0.04982 SPS-District K-12 0.04421 Campus 0.00842 Name: BuildingType, dtype: float64 ################################ CouncilDistrictCode 7 0.32702 2 0.24421 3 0.11789 4 0.09825 5 0.07509 6 0.07439 1 0.06316 Name: CouncilDistrictCode, dtype: float64 ################################ Neighborhood DOWNTOWN 0.22175 GREATER DUWAMISH 0.20842 LAKE UNION 0.09895 MAGNOLIA / QUEEN ANNE 0.09404 EAST 0.06877 NORTHEAST 0.06877 NORTHWEST 0.04912 BALLARD 0.04632 NORTH 0.03719 CENTRAL 0.03298 SOUTHEAST 0.02737 SOUTHWEST 0.02386 DELRIDGE 0.02246 Name: Neighborhood, dtype: float64 ################################ ################################
J'applique une transformation OneHotEncoder pour ces 4 variables.
Je conserve également l'ensemble de leurs modalités compte tenu de leur faible cardinalité.
Je conserve l'ensemble des modalités des variables suivantes car
leurs modalités ne représentent pas l'ensemble des modalités existantes réellement:
Je supprime la modalité la moins représentée pour
la variable suivante car l'ensemble des modalités possibles est représenté.
La dernière modalité pourra donc être déduite :
J'utilise ma fonction sans réduire les features en renseignant
l'argument percentFeatures à 1
et en laissant delLastModIfNoDummyValue à False.
df = reducFeaturesAndOneHotEncode(df, ['Neighborhood','CouncilDistrictCode','BuildingType'], percentFeatures=1)
Neighborhood : Aucune colonne effacée. CouncilDistrictCode : Aucune colonne effacée. BuildingType : Aucune colonne effacée.
J'utilise maintenant ma fonction en renseignant
l'argument percentFeatures à 1
mais et en paramétrant delLastModIfNoDummyValue à True.
df = reducFeaturesAndOneHotEncode(df, 'ComplianceStatus', percentFeatures=1, delLastModIfNoDummyValue=True)
ComplianceStatus : Colonne effacée ayant le moins de modalités : Non-Compliant
Aperçu des variables encodées :
df.filter(regex='^Nei|^Coun|^Build', axis=1).head().T
| 0 | 1 | 5 | 6 | 7 | |
|---|---|---|---|---|---|
| Neighborhood_BALLARD | 0 | 0 | 0 | 0 | 0 |
| Neighborhood_CENTRAL | 0 | 0 | 0 | 0 | 0 |
| Neighborhood_DELRIDGE | 0 | 0 | 0 | 0 | 0 |
| Neighborhood_DOWNTOWN | 1 | 1 | 1 | 1 | 1 |
| Neighborhood_EAST | 0 | 0 | 0 | 0 | 0 |
| Neighborhood_GREATER DUWAMISH | 0 | 0 | 0 | 0 | 0 |
| Neighborhood_LAKE UNION | 0 | 0 | 0 | 0 | 0 |
| Neighborhood_MAGNOLIA / QUEEN ANNE | 0 | 0 | 0 | 0 | 0 |
| Neighborhood_NORTH | 0 | 0 | 0 | 0 | 0 |
| Neighborhood_NORTHEAST | 0 | 0 | 0 | 0 | 0 |
| Neighborhood_NORTHWEST | 0 | 0 | 0 | 0 | 0 |
| Neighborhood_SOUTHEAST | 0 | 0 | 0 | 0 | 0 |
| Neighborhood_SOUTHWEST | 0 | 0 | 0 | 0 | 0 |
| CouncilDistrictCode_1 | 0 | 0 | 0 | 0 | 0 |
| CouncilDistrictCode_2 | 0 | 0 | 0 | 0 | 0 |
| CouncilDistrictCode_3 | 0 | 0 | 0 | 0 | 0 |
| CouncilDistrictCode_4 | 0 | 0 | 0 | 0 | 0 |
| CouncilDistrictCode_5 | 0 | 0 | 0 | 0 | 0 |
| CouncilDistrictCode_6 | 0 | 0 | 0 | 0 | 0 |
| CouncilDistrictCode_7 | 1 | 1 | 1 | 1 | 1 |
| BuildingType_Campus | 0 | 0 | 0 | 0 | 0 |
| BuildingType_NonResidential | 1 | 1 | 0 | 1 | 1 |
| BuildingType_Nonresidential COS | 0 | 0 | 1 | 0 | 0 |
| BuildingType_SPS-District K-12 | 0 | 0 | 0 | 0 | 0 |
Je mets à jour ma list et dictionnaire de variables catégorielles et continues
listVarCatCont = [[j for j in i if j in df.columns] for i in listVarCatCont]
dictVarCatCont = {'Ordre_Distance': listVarCatCont[0],
'NoDistance_Ordre':listVarCatCont[1],
'NoDistance_NoOrdre':listVarCatCont[2]}
dictVarCatCont
{'Ordre_Distance': ['ageBuilding',
'NumberofFloors',
'PropertyGFATotal',
'PropertyGFAParking',
'PropertyGFABuilding',
'LargestPropertyUseTypeGFA',
'SecondLargestPropertyUseTypeGFA',
'ThirdLargestPropertyUseTypeGFA',
'ENERGYSTARScore',
'Latitude',
'Longitude',
'Proportion_Electricity',
'Proportion_NaturalGas',
'Proportion_Steam',
'TotalGHGEmissions',
'SiteEnergyUse',
'NbListOfAllPropertyUseTypes'],
'NoDistance_Ordre': ['Outlier', 'DefaultData'],
'NoDistance_NoOrdre': ['PrimaryPropertyType',
'Address',
'ZipCode',
'LargestPropertyUseType',
'SecondLargestPropertyUseType',
'ThirdLargestPropertyUseType']}
Ici seule la variable 'Address' a une aussi haute cardinalité.
Les adresses étant très spécifiques, allant jusqu'au numéro de rue,
celà donne à cette variable une cardinalité proche de celle qu’aurait une variable d’ID.
Il est cependant possible de diminuer sa cardinalité en tentant
d'extraire les seuls noms de rue de chaque adresse.
Ensuite nous analyserons de nouveau cette variable
et jugerons s'il est utile de l’encoder ou non.
Rappelons la cardinalité de la variable '**Adress**'
ainsi que les dimensions du DataFrame '**df**' :
print('Cardinalité de la variable \'Address\':',df.Address.nunique())
print('Dimensions de \'df\': ',df.shape)
Cardinalité de la variable 'Address': 1364 Dimensions de 'df': (1425, 72)
Sans traitement préalable, l'adresse la plus représentée
représente moins de 0.5% des adresse existantes.
print('################################')
print('Address')
print(df.Address.value_counts(normalize=True).cumsum())
print('################################')
################################
Address
2623 NE UNIVERSITY VILLAGE ST 0.00491
2203 AIRPORT WAY S 0.00842
305 HARRISON ST 0.01053
516 1ST AVE W 0.01263
309 S CLOVERDALE ST 0.01474
...
2815 2ND AVE 0.99719
13000 AURORA AVE N 0.99789
13200 AURORA AVE N 0.99860
2101 NW 77TH ST 0.99930
10113 GREENWOOD AVE N 1.00000
Name: Address, Length: 1364, dtype: float64
################################
La fonction que j'ai écris ci-dessous tente d'extraire
les seuls noms de rue d'une adresse donnée.
L'idée est de supprimer, à l'exception du nom,
tous les élements de l'adresse comme :
def fromAddressToStreetName(addr):
'''Cette fonction tente d'extraire et de retourner
l'unique nom de rue d'un champ adresse au format US'''
if ((addr.split(' ')[0]).isdigit()):
addr = ' '.join(addr.split(' ')[1:]) # Suppression du numéro de rue
if not any(x in addr for x in ['1ST','21ST','31ST','41ST','51ST']):
addr = addr.replace('ST ', '')
addr = addr.replace(' ST', '')
if not any(x in addr for x in ['{}3RD'.format(i).replace('0','') for i in range(10) if i not in [1]]):
addr.replace(' RD','')
addr = addr.replace('WAY', '')
if not 'STONE' in addr:
addr = addr.replace('NE', '')
addr = addr.replace('NW', '')
addr = addr.replace('SE', '')
addr = addr.replace('SW', '')
addr = addr.replace('NORTH', '')
addr = addr.replace('SOUTH', '')
addr = addr.replace('EAST', '')
addr = addr.replace('WEST', '')
addr = addr.replace('AVE', '')
addr = addr.replace('AV', '')
addr = addr.replace('PL', '')
addr = ' '.join( [i for i in addr.split() if len(i)>1] ) # Suppression des lettres isolées
return addr
La cardinalité de la colonne 'Address' est maintenant réduite à 284.
df.Address.apply(lambda row: fromAddressToStreetName(row)).nunique()
282
Après traitement, l'adresse le plus représentée représente plus de 7%
des adresses existantes dans notre jeu de données.
df.Address.apply(lambda row: fromAddressToStreetName(row)).value_counts(normalize=True).cumsum().head(30)
1ST 0.07158 4TH 0.10737 LAKE 0.14105 6TH 0.17474 3RD 0.20491 2ND 0.23298 5TH 0.25684 AIRPORT 0.27439 MARGINAL 0.29053 AURORA 0.30596 PIKE 0.32000 ELLIOTT 0.33404 15TH 0.34737 34TH 0.36000 TERRY 0.37193 ERN 0.38316 MADISON 0.39368 RAINIER 0.40421 LAKE CITY 0.41404 FAIRVIEW 0.42316 ROOVELT 0.43228 8TH 0.44140 JACKSON 0.45053 CALIFORNIA 0.45895 PI 0.46737 ALASKAN 0.47579 BROAD 0.48421 9TH 0.49263 45TH 0.50105 HARRISON 0.50877 Name: Address, dtype: float64
Malgré l'efficacité du traitement effectué sur cette variable,
je choisis de ne pas l'encoder et de l'effacer.
En effet plusieurs mêmes noms de rue coexistent au sein de la même ville,
avec des CP différents, ce qui rend imprécise la solution appliquée ci-dessus.
L'ajout d'une contrainte avec la variable 'ZipCode' sur les différentiations
d'adresses réaugmenteraient davantage la cardinalité de la variable 'Addresse'.
Même s'il est possible d'encoder ce genre de variable avec
des transformeur de type HashingEncoder qui diminue le nombre
de colonnes générées par rapport à un One-Hot-Encoder,
je décide simplement de retirer la variable ‘Address’ de mon DataFrame 'df'.
df.drop('Address', axis=1, inplace=True)
Je mets à jour ma liste des variables sans notion de distance ni d'ordre
en retirant les six variables :
dictVarCatCont['NoDistance_NoOrdre'] = [i for i in dictVarCatCont['NoDistance_NoOrdre'] if i not in ['ComplianceStatus',
'BuildingType',
'CouncilDistrictCode',
'Neighborhood',
'Address']]
Il nous reste à encoder les variables suivantes :
dictVarCatCont['NoDistance_NoOrdre']
['PrimaryPropertyType', 'ZipCode', 'LargestPropertyUseType', 'SecondLargestPropertyUseType', 'ThirdLargestPropertyUseType']
Visualisons la fréquence relative
des modalités des variables à encoder :
print('################################')
for i in dictVarCatCont['NoDistance_NoOrdre']:
print('################################')
print(i)
print(df[i].value_counts(normalize=True).cumsum())
print('################################')
print('################################')
################################ ################################ PrimaryPropertyType Small- and Mid-Sized Office 0.19368 Other 0.34105 Large Office 0.45333 Non-Refrigerated Warehouse 0.56351 Mixed Use Property 0.64000 K-12 School 0.70456 Retail Store 0.75439 Hotel 0.79789 Worship Facility 0.83860 Distribution Center 0.87158 Medical Office 0.89754 Supermarket/Grocery Store 0.91509 Senior Care Community 0.93193 Self-Storage Facility 0.94807 Warehouse 0.95719 Residence Hall/Dormitory 0.96561 Restaurant 0.97333 Refrigerated Warehouse 0.98105 College/University 0.98807 Residence Hall 0.99298 Supermarket / Grocery Store 0.99509 Office 0.99719 Low-Rise Multifamily 0.99860 Laboratory 0.99930 University 1.00000 Name: PrimaryPropertyType, dtype: float64 ################################ ZipCode 98134 0.12281 98104 0.22035 98109 0.30807 98101 0.39368 98108 0.46667 98122 0.52000 98105 0.57123 98121 0.62035 98103 0.66737 98119 0.71158 98107 0.74526 98133 0.77614 98118 0.80632 98125 0.83579 98144 0.86386 98115 0.89053 98102 0.91228 98106 0.92842 98112 0.94316 98116 0.95719 98117 0.96842 98199 0.97965 98126 0.98947 98195 0.99298 98136 0.99509 98146 0.99649 98155 0.99789 98165 0.99860 98177 0.99930 98185 1.00000 Name: ZipCode, dtype: float64 ################################ LargestPropertyUseType Office 0.32982 Non-Refrigerated Warehouse 0.45754 K-12 School 0.52211 Retail Store 0.57754 Other 0.63158 Hotel 0.67649 Worship Facility 0.71719 Distribution Center 0.75088 Medical Office 0.77895 Supermarket/Grocery Store 0.80000 Other - Recreation 0.81895 Senior Care Community 0.83579 Self-Storage Facility 0.85193 Parking 0.86737 Residence Hall/Dormitory 0.88211 Other - Entertainment/Public Assembly 0.89193 Multifamily Housing 0.90035 Restaurant 0.90877 Refrigerated Warehouse 0.91649 College/University 0.92421 Social/Meeting Hall 0.93123 Laboratory 0.93825 Museum 0.94316 Automobile Dealership 0.94737 Other - Lodging/Residential 0.95088 Manufacturing/Industrial Plant 0.95439 Other - Services 0.95719 Other - Education 0.96000 Urgent Care/Clinic/Other Outpatient 0.96281 Repair Services (Vehicle, Shoe, Locksmith, etc) 0.96561 Strip Mall 0.96842 Bank Branch 0.97123 Library 0.97404 Other/Specialty Hospital 0.97614 Other - Mall 0.97825 Fitness Center/Health Club/Gym 0.98035 Prison/Incarceration 0.98246 Financial Office 0.98456 Performing Arts 0.98667 Pre-school/Daycare 0.98807 Other - Restaurant/Bar 0.98947 Data Center 0.99088 Adult Education 0.99228 Police Station 0.99298 Personal Services (Health/Beauty, Dry Cleaning, etc) 0.99368 Outpatient Rehabilitation/Physical Therapy 0.99439 Wholesale Club/Supercenter 0.99509 Courthouse 0.99579 Movie Theater 0.99649 Residential Care Facility 0.99719 Lifestyle Center 0.99789 Food Service 0.99860 Other - Public Services 0.99930 Other - Utility 1.00000 Name: LargestPropertyUseType, dtype: float64 ################################ SecondLargestPropertyUseType Aucun ou Non Renseigné 0.45825 Parking 0.66456 Office 0.77193 Retail Store 0.82596 Other 0.85333 Non-Refrigerated Warehouse 0.87509 Restaurant 0.89404 Fitness Center/Health Club/Gym 0.90526 Other - Services 0.91439 Multifamily Housing 0.92281 Supermarket/Grocery Store 0.93053 Data Center 0.93825 Distribution Center 0.94386 Other - Entertainment/Public Assembly 0.94877 Medical Office 0.95368 Bank Branch 0.95860 K-12 School 0.96281 Laboratory 0.96632 Repair Services (Vehicle, Shoe, Locksmith, etc) 0.96912 Self-Storage Facility 0.97193 Movie Theater 0.97404 Bar/Nightclub 0.97614 Manufacturing/Industrial Plant 0.97825 Other - Recreation 0.98035 Other - Public Services 0.98246 Hotel 0.98456 Performing Arts 0.98596 Food Sales 0.98737 Adult Education 0.98877 Other - Education 0.99018 Swimming Pool 0.99158 Pre-school/Daycare 0.99228 Refrigerated Warehouse 0.99298 Food Service 0.99368 Convenience Store without Gas Station 0.99439 Financial Office 0.99509 Worship Facility 0.99579 Personal Services (Health/Beauty, Dry Cleaning, etc) 0.99649 Courthouse 0.99719 Vocational School 0.99789 Other - Lodging/Residential 0.99860 Other - Restaurant/Bar 0.99930 Residence Hall/Dormitory 1.00000 Name: SecondLargestPropertyUseType, dtype: float64 ################################ ThirdLargestPropertyUseType Aucun ou Non Renseigné 0.76982 Office 0.80491 Retail Store 0.83649 Parking 0.86807 Other 0.89263 Restaurant 0.91298 Non-Refrigerated Warehouse 0.92281 Multifamily Housing 0.93193 Data Center 0.93895 Swimming Pool 0.94526 Medical Office 0.95018 Food Service 0.95509 Pre-school/Daycare 0.96000 Social/Meeting Hall 0.96421 Bank Branch 0.96772 Financial Office 0.97053 Other - Entertainment/Public Assembly 0.97333 Laboratory 0.97544 Other - Recreation 0.97754 Fitness Center/Health Club/Gym 0.97965 Distribution Center 0.98175 Other - Restaurant/Bar 0.98386 Other - Services 0.98526 Self-Storage Facility 0.98667 Refrigerated Warehouse 0.98807 Convenience Store without Gas Station 0.98947 Worship Facility 0.99088 K-12 School 0.99228 Personal Services (Health/Beauty, Dry Cleaning, etc) 0.99368 Vocational School 0.99439 Hotel 0.99509 Fast Food Restaurant 0.99579 Other - Technology/Science 0.99649 Enclosed Mall 0.99719 Manufacturing/Industrial Plant 0.99789 Other - Education 0.99860 Bar/Nightclub 0.99930 Supermarket/Grocery Store 1.00000 Name: ThirdLargestPropertyUseType, dtype: float64 ################################ ################################
J'applique une transformation **OneHotEncoder** pour ces 5 variables
en appliquant la stratégie suivante :
Je conserve 100% des modalités pour la variable :
Je conserve les modalités qui représentent 98%
des échantillons pour les variables :
df = reducFeaturesAndOneHotEncode(df,'PrimaryPropertyType', percentFeatures=1.0)
df = reducFeaturesAndOneHotEncode(df,['LargestPropertyUseType','SecondLargestPropertyUseType','ThirdLargestPropertyUseType','ZipCode'], percentFeatures=0.98)
PrimaryPropertyType : Colonne effacée ayant le moins de modalités : A_Supprimer LargestPropertyUseType : Colonne effacée ayant le moins de modalités : A_Supprimer SecondLargestPropertyUseType : Colonne effacée ayant le moins de modalités : A_Supprimer ThirdLargestPropertyUseType : Colonne effacée ayant le moins de modalités : A_Supprimer ZipCode : Colonne effacée ayant le moins de modalités : 99999
df.T
| 0 | 1 | 5 | 6 | 7 | 8 | 10 | 12 | 15 | 16 | 17 | 18 | 19 | 20 | 23 | 24 | 25 | 26 | 28 | 29 | 32 | 33 | 36 | 38 | 39 | 40 | 41 | 43 | 46 | 48 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 60 | 61 | 63 | 65 | 67 | 68 | 70 | 76 | 78 | 79 | 80 | 82 | 83 | 84 | 87 | 88 | 89 | 90 | 91 | 95 | 96 | 97 | 100 | 101 | 102 | 103 | 106 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 117 | 118 | 119 | 121 | 124 | 125 | 128 | 129 | 130 | 131 | 132 | 133 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 150 | 152 | ... | 4668 | 4675 | 4766 | 4776 | 4805 | 4836 | 4848 | 4898 | 4930 | 4963 | 4977 | 4983 | 5094 | 5102 | 5106 | 5158 | 5265 | 5281 | 5290 | 5306 | 5333 | 5378 | 5427 | 5510 | 5542 | 5552 | 5594 | 5719 | 5723 | 5747 | 5750 | 5787 | 5859 | 5864 | 5875 | 5907 | 5924 | 5943 | 5954 | 5994 | 6024 | 6044 | 6079 | 6084 | 6118 | 6125 | 6140 | 6285 | 6381 | 6397 | 6398 | 6435 | 6438 | 6461 | 6470 | 6471 | 6503 | 6513 | 6516 | 6532 | 6538 | 6539 | 6543 | 6556 | 6558 | 6561 | 6568 | 6575 | 6594 | 6599 | 6607 | 6616 | 6618 | 6619 | 6620 | 6652 | 6657 | 6660 | 6664 | 6669 | 6677 | 6678 | 6679 | 6680 | 6700 | 6701 | 6702 | 6703 | 6704 | 6705 | 6706 | 6707 | 6708 | 6709 | 6710 | 6711 | 6712 | 6713 | 6714 | 6715 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NumberofFloors | 12.00000 | 11.00000 | 2.00000 | 11.00000 | 8.00000 | 15.00000 | 11.00000 | 9.00000 | 11.00000 | 6.00000 | 28.00000 | 5.00000 | 10.00000 | 10.00000 | 6.00000 | 7.00000 | 2.00000 | 8.00000 | 1.00000 | 5.00000 | 3.00000 | 4.00000 | 2.00000 | 2.00000 | 2.00000 | 3.00000 | 3.00000 | 5.00000 | 2.00000 | 20.00000 | 7.00000 | 5.00000 | 7.00000 | 6.00000 | 6.00000 | 3.00000 | 4.00000 | 2.00000 | 2.00000 | 6.00000 | 3.00000 | 6.00000 | 5.00000 | 7.00000 | 1.00000 | 2.00000 | 4.00000 | 6.00000 | 4.00000 | 3.00000 | 6.00000 | 3.00000 | 3.00000 | 2.00000 | 4.00000 | 2.00000 | 4.00000 | 3.00000 | 5.00000 | 6.00000 | 4.00000 | 4.00000 | 4.00000 | 4.00000 | 2.00000 | 3.00000 | 3.00000 | 3.00000 | 3.00000 | 1.00000 | 5.00000 | 2.00000 | 4.00000 | 6.00000 | 3.00000 | 3.00000 | 3.00000 | 3.00000 | 2.00000 | 2.00000 | 1.00000 | 4.00000 | 2.00000 | 1.00000 | 1.00000 | 2.00000 | 1.00000 | 3.00000 | 3.00000 | 2.00000 | 2.00000 | 3.00000 | 5.00000 | 1.00000 | 2.00000 | 7.00000 | 2.00000 | 2.00000 | 4.00000 | 1.00000 | ... | 4.00000 | 2.00000 | 2.00000 | 5.00000 | 1.00000 | 2.00000 | 2.00000 | 1.00000 | 4.00000 | 2.00000 | 2.00000 | 5.00000 | 0.00000 | 3.00000 | 1.00000 | 1.00000 | 2.00000 | 2.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 6.00000 | 1.00000 | 1.00000 | 2.00000 | 3.00000 | 5.00000 | 3.00000 | 2.00000 | 2.00000 | 16.00000 | 1.00000 | 3.00000 | 1.00000 | 4.00000 | 3.00000 | 2.00000 | 1.00000 | 4.00000 | 1.00000 | 3.00000 | 3.00000 | 2.00000 | 2.00000 | 1.00000 | 6.00000 | 2.00000 | 1.00000 | 11.00000 | 5.00000 | 3.00000 | 0.00000 | 0.00000 | 6.00000 | 6.00000 | 7.00000 | 3.00000 | 1.00000 | 13.00000 | 7.00000 | 3.00000 | 3.00000 | 4.00000 | 6.00000 | 7.00000 | 1.00000 | 2.00000 | 3.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 5.00000 | 10.00000 | 12.00000 | 12.00000 | 13.00000 | 4.00000 | 1.00000 | 2.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 |
| PropertyGFATotal | 88434.00000 | 103566.00000 | 97288.00000 | 83008.00000 | 102761.00000 | 163984.00000 | 153163.00000 | 65009.00000 | 412000.00000 | 103911.00000 | 416281.00000 | 57452.00000 | 104352.00000 | 540360.00000 | 88592.00000 | 126593.00000 | 67224.00000 | 158676.00000 | 126351.00000 | 68410.00000 | 87262.00000 | 81871.00000 | 54986.00000 | 94900.00000 | 52554.00000 | 175015.00000 | 70586.00000 | 168115.00000 | 52549.00000 | 98634.00000 | 122810.00000 | 113173.00000 | 150453.00000 | 116300.00000 | 155602.00000 | 56072.00000 | 122942.00000 | 54984.00000 | 93397.00000 | 84103.00000 | 179014.00000 | 187600.00000 | 316306.00000 | 282863.00000 | 91213.00000 | 66240.00000 | 76213.00000 | 117738.00000 | 50017.00000 | 67743.00000 | 194104.00000 | 99433.00000 | 59955.00000 | 93218.00000 | 56521.00000 | 206934.00000 | 51390.00000 | 66588.00000 | 190980.00000 | 284100.00000 | 61156.00000 | 72820.00000 | 162222.00000 | 289588.00000 | 96300.00000 | 254523.00000 | 78804.00000 | 66994.00000 | 98357.00000 | 54765.00000 | 89821.00000 | 52611.00000 | 139600.00000 | 250981.00000 | 62526.00000 | 64015.00000 | 54224.00000 | 55442.00000 | 242795.00000 | 173185.00000 | 145341.00000 | 114297.00000 | 184125.00000 | 160270.00000 | 234144.00000 | 367884.00000 | 46797.00000 | 136188.00000 | 56000.00000 | 67371.00000 | 52088.00000 | 127776.00000 | 123920.00000 | 58000.00000 | 50292.00000 | 207656.00000 | 67235.00000 | 85017.00000 | 64893.00000 | 70762.00000 | ... | 274442.00000 | 85750.00000 | 54528.00000 | 43594.00000 | 22176.00000 | 26560.00000 | 38845.00000 | 40445.00000 | 42500.00000 | 57644.00000 | 27574.00000 | 43661.00000 | 274568.00000 | 33580.00000 | 21936.00000 | 41308.00000 | 71919.00000 | 21592.00000 | 160000.00000 | 20724.00000 | 230971.00000 | 39600.00000 | 25658.00000 | 51000.00000 | 244500.00000 | 25384.00000 | 23903.00000 | 26519.00000 | 23355.00000 | 57600.00000 | 25450.00000 | 36940.00000 | 21562.00000 | 153312.00000 | 24200.00000 | 60000.00000 | 141297.00000 | 41704.00000 | 27600.00000 | 78716.00000 | 20880.00000 | 56896.00000 | 21624.00000 | 51360.00000 | 27487.00000 | 22344.00000 | 32928.00000 | 24955.00000 | 97229.00000 | 31805.00000 | 20811.00000 | 68452.00000 | 54015.00000 | 45743.00000 | 384772.00000 | 30287.00000 | 74211.00000 | 157345.00000 | 192259.00000 | 43832.00000 | 67263.00000 | 354086.00000 | 90679.00000 | 105061.00000 | 62662.00000 | 62381.00000 | 79805.00000 | 333714.00000 | 53775.00000 | 64820.00000 | 197395.00000 | 176225.00000 | 166991.00000 | 89392.00000 | 97040.00000 | 37873.00000 | 513816.00000 | 480702.00000 | 483397.00000 | 536697.00000 | 126823.00000 | 52085.00000 | 24990.00000 | 72000.00000 | 20616.00000 | 11685.00000 | 11968.00000 | 11285.00000 | 16795.00000 | 12769.00000 | 13661.00000 | 23445.00000 | 20050.00000 | 15398.00000 | 18261.00000 | 12294.00000 | 16000.00000 | 13157.00000 | 14101.00000 | 18258.00000 |
| PropertyGFAParking | 0.00000 | 15064.00000 | 37198.00000 | 0.00000 | 0.00000 | 0.00000 | 19279.00000 | 0.00000 | 57000.00000 | 0.00000 | 85000.00000 | 0.00000 | 0.00000 | 0.00000 | 30586.00000 | 0.00000 | 0.00000 | 30301.00000 | 0.00000 | 16200.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 10043.00000 | 0.00000 | 42546.00000 | 34735.00000 | 28200.00000 | 36744.00000 | 0.00000 | 44891.00000 | 0.00000 | 0.00000 | 20732.00000 | 0.00000 | 18493.00000 | 0.00000 | 44766.00000 | 0.00000 | 2352.00000 | 25930.00000 | 36934.00000 | 0.00000 | 0.00000 | 0.00000 | 35752.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 14200.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 37500.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 30000.00000 | 0.00000 | 0.00000 | 54341.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 65640.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 12990.00000 | 0.00000 | 0.00000 | 14083.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 8584.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 149326.00000 | 35120.00000 | 28217.00000 | 14989.00000 | 22948.00000 | 0.00000 | 146687.00000 | 0.00000 | 0.00000 | 156000.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 163260.00000 | 166208.00000 | 197659.00000 | 41539.00000 | 4222.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| PropertyGFABuilding | 88434.00000 | 88502.00000 | 60090.00000 | 83008.00000 | 102761.00000 | 163984.00000 | 133884.00000 | 65009.00000 | 355000.00000 | 103911.00000 | 331281.00000 | 57452.00000 | 104352.00000 | 540360.00000 | 58006.00000 | 126593.00000 | 67224.00000 | 128375.00000 | 126351.00000 | 52210.00000 | 87262.00000 | 81871.00000 | 54986.00000 | 94900.00000 | 52554.00000 | 175015.00000 | 70586.00000 | 168115.00000 | 52549.00000 | 88591.00000 | 122810.00000 | 70627.00000 | 115718.00000 | 88100.00000 | 118858.00000 | 56072.00000 | 78051.00000 | 54984.00000 | 93397.00000 | 63371.00000 | 179014.00000 | 169107.00000 | 316306.00000 | 238097.00000 | 91213.00000 | 63888.00000 | 50283.00000 | 80804.00000 | 50017.00000 | 67743.00000 | 194104.00000 | 63681.00000 | 59955.00000 | 93218.00000 | 56521.00000 | 206934.00000 | 51390.00000 | 66588.00000 | 190980.00000 | 284100.00000 | 61156.00000 | 72820.00000 | 148022.00000 | 289588.00000 | 96300.00000 | 254523.00000 | 78804.00000 | 66994.00000 | 98357.00000 | 54765.00000 | 89821.00000 | 52611.00000 | 102100.00000 | 250981.00000 | 62526.00000 | 64015.00000 | 54224.00000 | 55442.00000 | 242795.00000 | 173185.00000 | 145341.00000 | 114297.00000 | 184125.00000 | 160270.00000 | 234144.00000 | 367884.00000 | 46797.00000 | 136188.00000 | 56000.00000 | 67371.00000 | 52088.00000 | 127776.00000 | 93920.00000 | 58000.00000 | 50292.00000 | 153315.00000 | 67235.00000 | 85017.00000 | 64893.00000 | 70762.00000 | ... | 274442.00000 | 20110.00000 | 54528.00000 | 43594.00000 | 22176.00000 | 26560.00000 | 38845.00000 | 40445.00000 | 42500.00000 | 57644.00000 | 27574.00000 | 43661.00000 | 274568.00000 | 20590.00000 | 21936.00000 | 41308.00000 | 57836.00000 | 21592.00000 | 160000.00000 | 20724.00000 | 230971.00000 | 39600.00000 | 25658.00000 | 51000.00000 | 244500.00000 | 25384.00000 | 23903.00000 | 26519.00000 | 23355.00000 | 57600.00000 | 25450.00000 | 36940.00000 | 21562.00000 | 153312.00000 | 24200.00000 | 60000.00000 | 141297.00000 | 33120.00000 | 27600.00000 | 78716.00000 | 20880.00000 | 56896.00000 | 21624.00000 | 51360.00000 | 27487.00000 | 22344.00000 | 32928.00000 | 24955.00000 | 97229.00000 | 31805.00000 | 20811.00000 | 68452.00000 | 54015.00000 | 45743.00000 | 384772.00000 | 30287.00000 | 74211.00000 | 157345.00000 | 192259.00000 | 43832.00000 | 67263.00000 | 204760.00000 | 55559.00000 | 76844.00000 | 47673.00000 | 39433.00000 | 79805.00000 | 187027.00000 | 53775.00000 | 64820.00000 | 41395.00000 | 176225.00000 | 166991.00000 | 89392.00000 | 97040.00000 | 37873.00000 | 513816.00000 | 317442.00000 | 317189.00000 | 339038.00000 | 85284.00000 | 47863.00000 | 24990.00000 | 72000.00000 | 20616.00000 | 11685.00000 | 11968.00000 | 11285.00000 | 16795.00000 | 12769.00000 | 13661.00000 | 23445.00000 | 20050.00000 | 15398.00000 | 18261.00000 | 12294.00000 | 16000.00000 | 13157.00000 | 14101.00000 | 18258.00000 |
| LargestPropertyUseTypeGFA | 88434.00000 | 83880.00000 | 88830.00000 | 81352.00000 | 102761.00000 | 163984.00000 | 83591.00000 | 65009.00000 | 364913.00000 | 90000.00000 | 348329.00000 | 16442.00000 | 83591.00000 | 537150.00000 | 58006.00000 | 125000.00000 | 67224.00000 | 112676.00000 | 126351.00000 | 47994.00000 | 40943.00000 | 81871.00000 | 54986.00000 | 55586.00000 | 51029.00000 | 175015.00000 | 38439.00000 | 103501.00000 | 23500.00000 | 83591.00000 | 88360.00000 | 83591.00000 | 107547.00000 | 88490.00000 | 155602.00000 | 56072.00000 | 78051.00000 | 54984.00000 | 93397.00000 | 63371.00000 | 179014.00000 | 40174.00000 | 261826.00000 | 235788.00000 | 65000.00000 | 45900.00000 | 48546.00000 | 59976.00000 | 50017.00000 | 67743.00000 | 194104.00000 | 63681.00000 | 59955.00000 | 93218.00000 | 49000.00000 | 119146.00000 | 51390.00000 | 66588.00000 | 190980.00000 | 189750.00000 | 42755.00000 | 58000.00000 | 113702.00000 | 289588.00000 | 92300.00000 | 254523.00000 | 78804.00000 | 66994.00000 | 98357.00000 | 25765.00000 | 46483.00000 | 52611.00000 | 135520.00000 | 137565.00000 | 62526.00000 | 30000.00000 | 47012.00000 | 28636.00000 | 242795.00000 | 173185.00000 | 139069.00000 | 114297.00000 | 184125.00000 | 160270.00000 | 234144.00000 | 367884.00000 | 46797.00000 | 136188.00000 | 56000.00000 | 67371.00000 | 52088.00000 | 127776.00000 | 123920.00000 | 43000.00000 | 42345.00000 | 141591.00000 | 67235.00000 | 57968.00000 | 64893.00000 | 70762.00000 | ... | 108763.00000 | 65000.00000 | 54285.00000 | 40809.00000 | 22176.00000 | 26560.00000 | 38845.00000 | 40445.00000 | 42500.00000 | 20176.00000 | 27574.00000 | 43661.00000 | 228133.00000 | 20590.00000 | 21936.00000 | 41308.00000 | 34550.00000 | 20700.00000 | 160000.00000 | 19224.00000 | 230970.00000 | 39600.00000 | 18854.00000 | 51000.00000 | 244500.00000 | 23468.00000 | 18350.00000 | 13592.00000 | 21915.00000 | 27600.00000 | 25450.00000 | 36940.00000 | 21052.00000 | 148590.00000 | 24200.00000 | 48783.00000 | 141297.00000 | 41704.00000 | 13175.00000 | 41275.00000 | 20880.00000 | 40768.00000 | 21624.00000 | 51026.00000 | 27487.00000 | 22344.00000 | 24288.00000 | 24955.00000 | 45300.00000 | 31805.00000 | 20811.00000 | 61665.00000 | 54015.00000 | 45243.00000 | 349953.00000 | 28050.00000 | 33279.00000 | 157345.00000 | 188118.00000 | 24588.00000 | 67263.00000 | 193393.00000 | 60000.00000 | 83533.00000 | 47000.00000 | 60245.00000 | 79805.00000 | 191333.00000 | 35361.00000 | 37668.00000 | 38800.00000 | 176225.00000 | 166991.00000 | 89392.00000 | 97040.00000 | 25927.00000 | 359040.00000 | 341897.00000 | 342173.00000 | 342838.00000 | 88157.00000 | 52085.00000 | 24990.00000 | 72000.00000 | 9900.00000 | 11685.00000 | 11968.00000 | 11285.00000 | 8680.00000 | 10912.00000 | 13661.00000 | 23445.00000 | 8108.00000 | 15398.00000 | 18261.00000 | 12294.00000 | 16000.00000 | 7583.00000 | 6601.00000 | 8271.00000 |
| SecondLargestPropertyUseTypeGFA | 0.00000 | 15064.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 49000.00000 | 40000.00000 | 85000.00000 | 15505.00000 | 0.00000 | 0.00000 | 30586.00000 | 0.00000 | 0.00000 | 42000.00000 | 0.00000 | 0.00000 | 18153.00000 | 0.00000 | 0.00000 | 39314.00000 | 7849.00000 | 0.00000 | 28197.00000 | 65676.00000 | 23500.00000 | 10043.00000 | 26450.00000 | 0.00000 | 3000.00000 | 28300.00000 | 0.00000 | 0.00000 | 44891.00000 | 0.00000 | 0.00000 | 20732.00000 | 0.00000 | 20868.00000 | 150726.00000 | 51537.00000 | 0.00000 | 5181.00000 | 21454.00000 | 36934.00000 | 0.00000 | 0.00000 | 0.00000 | 35752.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 19051.00000 | 0.00000 | 29219.00000 | 0.00000 | 32692.00000 | 0.00000 | 4944.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 25000.00000 | 37938.00000 | 0.00000 | 0.00000 | 78357.00000 | 0.00000 | 15000.00000 | 0.00000 | 15385.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 30000.00000 | 0.00000 | 5000.00000 | 0.00000 | 0.00000 | 32881.00000 | 0.00000 | 0.00000 | ... | 105499.00000 | 20000.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 6000.00000 | 0.00000 | 5091.00000 | 19440.00000 | 0.00000 | 0.00000 | 135954.00000 | 12990.00000 | 0.00000 | 0.00000 | 23085.00000 | 0.00000 | 0.00000 | 1500.00000 | 0.00000 | 0.00000 | 6804.00000 | 0.00000 | 0.00000 | 0.00000 | 4500.00000 | 12927.00000 | 0.00000 | 16000.00000 | 0.00000 | 0.00000 | 400.00000 | 72317.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 9175.00000 | 29399.00000 | 0.00000 | 16128.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 16710.00000 | 0.00000 | 35973.00000 | 0.00000 | 0.00000 | 6787.00000 | 0.00000 | 0.00000 | 63872.00000 | 0.00000 | 29878.00000 | 0.00000 | 7000.00000 | 17558.00000 | 0.00000 | 149326.00000 | 30700.00000 | 28217.00000 | 0.00000 | 26067.00000 | 7000.00000 | 133432.00000 | 12689.00000 | 11500.00000 | 3650.00000 | 24000.00000 | 0.00000 | 0.00000 | 0.00000 | 5834.00000 | 151658.00000 | 136594.00000 | 147597.00000 | 202178.00000 | 41539.00000 | 0.00000 | 0.00000 | 0.00000 | 8577.00000 | 0.00000 | 0.00000 | 0.00000 | 7014.00000 | 1670.00000 | 0.00000 | 0.00000 | 7726.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 5574.00000 | 6501.00000 | 8000.00000 |
| ThirdLargestPropertyUseTypeGFA | 0.00000 | 4622.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1074.00000 | 0.00000 | 0.00000 | 9741.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 4500.00000 | 0.00000 | 0.00000 | 4307.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 3859.00000 | 354.00000 | 5459.00000 | 5000.00000 | 8000.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 17020.00000 | 15139.00000 | 0.00000 | 0.00000 | 4551.00000 | 2406.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 4579.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 9015.00000 | 0.00000 | 8644.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 4730.00000 | 0.00000 | 0.00000 | 11850.00000 | 0.00000 | 0.00000 | ... | 58152.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 15490.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 11465.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 7000.00000 | 0.00000 | 0.00000 | 0.00000 | 4722.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 6278.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 16128.00000 | 0.00000 | 16956.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 8421.00000 | 0.00000 | 0.00000 | 1686.00000 | 0.00000 | 38225.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 2110.00000 | 10000.00000 | 586.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 5160.00000 | 1087.00000 | 0.00000 | 0.00000 | 6313.20020 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1364.00000 | 0.00000 | 0.00000 | 0.00000 | 535.00000 | 187.00000 | 0.00000 | 0.00000 | 3779.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 484.00000 | 1108.00000 |
| ENERGYSTARScore | 65.00000 | 51.00000 | nan | 25.00000 | nan | 46.00000 | 48.00000 | 39.00000 | nan | nan | 30.00000 | nan | 83.00000 | 67.00000 | 85.00000 | nan | nan | nan | 57.00000 | 60.00000 | nan | 20.00000 | 91.00000 | 57.00000 | 93.00000 | 95.00000 | nan | 79.00000 | nan | 46.00000 | 99.00000 | 86.00000 | 83.00000 | 84.00000 | 81.00000 | 77.00000 | 94.00000 | 65.00000 | 1.00000 | 68.00000 | 48.00000 | nan | 96.00000 | 40.00000 | nan | nan | 72.00000 | 52.00000 | 51.00000 | 43.00000 | 100.00000 | 71.00000 | 94.00000 | 84.00000 | nan | nan | 86.00000 | 75.00000 | 97.00000 | nan | nan | nan | 75.00000 | 92.00000 | nan | 62.00000 | 92.00000 | 83.00000 | 90.00000 | nan | nan | nan | 88.00000 | nan | 97.00000 | 19.00000 | nan | 74.00000 | 82.00000 | 72.00000 | 77.00000 | 67.00000 | 89.00000 | 79.00000 | 82.00000 | 93.00000 | 86.00000 | 67.00000 | nan | 92.00000 | nan | 81.00000 | 82.00000 | nan | nan | 77.00000 | 85.00000 | 40.00000 | 73.00000 | 95.00000 | ... | nan | nan | 75.00000 | 38.00000 | 60.00000 | 1.00000 | 82.00000 | 92.00000 | 100.00000 | nan | 73.00000 | 4.00000 | 52.00000 | 100.00000 | 100.00000 | 7.00000 | 37.00000 | 47.00000 | 94.00000 | nan | nan | 85.00000 | nan | 95.00000 | 100.00000 | 11.00000 | 65.00000 | nan | 77.00000 | nan | 99.00000 | 100.00000 | 20.00000 | 51.00000 | 73.00000 | 52.00000 | 76.00000 | 73.00000 | 84.00000 | 38.00000 | 94.00000 | 96.00000 | 90.00000 | 89.00000 | 82.00000 | 40.00000 | 9.00000 | 47.00000 | 100.00000 | 10.00000 | 95.00000 | 79.00000 | 75.00000 | 88.00000 | 69.00000 | 32.00000 | 97.00000 | 63.00000 | 72.00000 | 67.00000 | 78.00000 | 88.00000 | 100.00000 | 100.00000 | 67.00000 | 62.00000 | 25.00000 | nan | nan | nan | nan | 91.00000 | 19.00000 | 16.00000 | 69.00000 | nan | nan | 99.00000 | 98.00000 | 98.00000 | 72.00000 | 37.00000 | 9.00000 | nan | nan | nan | nan | nan | nan | nan | 75.00000 | nan | nan | 93.00000 | nan | 46.00000 | nan | nan | nan | nan |
| DefaultData | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | ... | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| Outlier | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| Latitude | 47.61219 | 47.61311 | 47.61644 | 47.61411 | 47.61290 | 47.60259 | 47.60712 | 47.60710 | 47.60668 | 47.60545 | 47.60656 | 47.60553 | 47.60398 | 47.60265 | 47.65953 | 47.61839 | 47.61491 | 47.61300 | 47.62309 | 47.61587 | 47.66392 | 47.66585 | 47.65726 | 47.66581 | 47.58003 | 47.68895 | 47.61311 | 47.61104 | 47.60883 | 47.61229 | 47.61155 | 47.62037 | 47.62031 | 47.62022 | 47.62051 | 47.63446 | 47.62498 | 47.60841 | 47.72389 | 47.61991 | 47.61250 | 47.60831 | 47.62428 | 47.62636 | 47.62124 | 47.62392 | 47.62038 | 47.62294 | 47.65775 | 47.62623 | 47.62596 | 47.58407 | 47.55446 | 47.63673 | 47.67530 | 47.67567 | 47.73127 | 47.65475 | 47.60477 | 47.60811 | 47.60158 | 47.59810 | 47.62557 | 47.57605 | 47.67844 | 47.60502 | 47.51466 | 47.61229 | 47.52934 | 47.53830 | 47.53830 | 47.65154 | 47.65076 | 47.60412 | 47.68734 | 47.53532 | 47.70625 | 47.70953 | 47.67679 | 47.54658 | 47.54806 | 47.54942 | 47.52383 | 47.69690 | 47.55861 | 47.52967 | 47.51791 | 47.59792 | 47.67771 | 47.54197 | 47.54506 | 47.65721 | 47.62970 | 47.62759 | 47.62652 | 47.62754 | 47.68367 | 47.69976 | 47.70923 | 47.53837 | ... | 47.61893 | 47.64132 | 47.54176 | 47.64132 | 47.51930 | 47.61010 | 47.53846 | 47.66327 | 47.66353 | 47.65118 | 47.70793 | 47.71069 | 47.60862 | 47.72986 | 47.72051 | 47.57460 | 47.60546 | 47.59692 | 47.55945 | 47.58032 | 47.59042 | 47.57294 | 47.70117 | 47.69061 | 47.70178 | 47.69274 | 47.59774 | 47.62902 | 47.63614 | 47.60050 | 47.60035 | 47.61478 | 47.68517 | 47.63429 | 47.53857 | 47.61339 | 47.53515 | 47.72900 | 47.61545 | 47.64748 | 47.60110 | 47.67819 | 47.71705 | 47.66828 | 47.66289 | 47.60320 | 47.66272 | 47.71479 | 47.61608 | 47.64725 | 47.64725 | 47.59706 | 47.62495 | 47.61379 | 47.68454 | 47.68240 | 47.60121 | 47.58995 | 47.65599 | 47.62503 | 47.57984 | 47.61156 | 47.61449 | 47.63449 | 47.59666 | 47.66629 | 47.61885 | 47.58872 | 47.52472 | 47.61438 | 47.60898 | 47.65718 | 47.65577 | 47.65447 | 47.65176 | 47.61371 | 47.62906 | 47.62146 | 47.62263 | 47.62251 | 47.65476 | 47.54425 | 47.53161 | 47.68109 | 47.70541 | 47.59905 | 47.69330 | 47.72126 | 47.67295 | 47.67734 | 47.63572 | 47.63228 | 47.60775 | 47.56440 | 47.54067 | 47.56722 | 47.59625 | 47.63644 | 47.52832 | 47.53939 |
| Longitude | -122.33800 | -122.33336 | -122.33676 | -122.33274 | -122.33131 | -122.33255 | -122.33432 | -122.33311 | -122.33226 | -122.33280 | -122.33064 | -122.33163 | -122.33206 | -122.33053 | -122.31739 | -122.33693 | -122.33040 | -122.32923 | -122.30539 | -122.34198 | -122.33172 | -122.29889 | -122.39065 | -122.38603 | -122.31181 | -122.33616 | -122.35097 | -122.33635 | -122.33742 | -122.33442 | -122.34085 | -122.34415 | -122.34280 | -122.34321 | -122.35969 | -122.30802 | -122.34456 | -122.30954 | -122.35630 | -122.33205 | -122.32134 | -122.32662 | -122.33589 | -122.33313 | -122.35343 | -122.34834 | -122.35843 | -122.35710 | -122.31753 | -122.30820 | -122.30416 | -122.30127 | -122.27640 | -122.35895 | -122.31534 | -122.31619 | -122.34624 | -122.34910 | -122.33678 | -122.33697 | -122.32772 | -122.32283 | -122.34720 | -122.29285 | -122.26417 | -122.30183 | -122.25882 | -122.29082 | -122.28844 | -122.28405 | -122.28405 | -122.36155 | -122.36380 | -122.31533 | -122.35984 | -122.31860 | -122.32175 | -122.33466 | -122.37397 | -122.28202 | -122.36200 | -122.31604 | -122.26601 | -122.37785 | -122.33323 | -122.36666 | -122.37068 | -122.30395 | -122.39623 | -122.27678 | -122.37736 | -122.33758 | -122.32906 | -122.33661 | -122.34084 | -122.34057 | -122.37292 | -122.34025 | -122.33237 | -122.29643 | ... | -122.34473 | -122.31644 | -122.37349 | -122.31644 | -122.32130 | -122.30838 | -122.28305 | -122.37439 | -122.38064 | -122.37875 | -122.33316 | -122.32674 | -122.32372 | -122.29278 | -122.30103 | -122.32637 | -122.34091 | -122.30601 | -122.33601 | -122.32083 | -122.32498 | -122.32811 | -122.34284 | -122.35731 | -122.32352 | -122.37602 | -122.31936 | -122.33173 | -122.33982 | -122.33514 | -122.33379 | -122.31847 | -122.38733 | -122.27652 | -122.28026 | -122.32594 | -122.26556 | -122.34473 | -122.32380 | -122.33435 | -122.33197 | -122.32310 | -122.28577 | -122.32660 | -122.30863 | -122.30596 | -122.31798 | -122.32824 | -122.30967 | -122.39977 | -122.39977 | -122.32813 | -122.36036 | -122.32143 | -122.26235 | -122.26223 | -122.33518 | -122.33472 | -122.31385 | -122.35642 | -122.38577 | -122.34401 | -122.33956 | -122.37661 | -122.31253 | -122.38260 | -122.30341 | -122.33475 | -122.27074 | -122.32174 | -122.34213 | -122.31632 | -122.31530 | -122.31733 | -122.30756 | -122.31774 | -122.34259 | -122.33976 | -122.33976 | -122.33390 | -122.34732 | -122.31776 | -122.29944 | -122.26180 | -122.32232 | -122.29787 | -122.37717 | -122.29735 | -122.39228 | -122.37624 | -122.37525 | -122.31574 | -122.30225 | -122.27813 | -122.37441 | -122.31154 | -122.32283 | -122.35784 | -122.32431 | -122.29536 |
| ageBuilding | 88.00000 | 19.00000 | 16.00000 | 89.00000 | 89.00000 | 111.00000 | 46.00000 | 87.00000 | 11.00000 | 85.00000 | 32.00000 | 108.00000 | 99.00000 | 99.00000 | 14.00000 | 87.00000 | 85.00000 | 24.00000 | 60.00000 | 37.00000 | 109.00000 | 21.00000 | 25.00000 | 54.00000 | 115.00000 | 61.00000 | 104.00000 | 42.00000 | 89.00000 | 85.00000 | 107.00000 | 56.00000 | 15.00000 | 18.00000 | 17.00000 | 104.00000 | 16.00000 | 53.00000 | 45.00000 | 7.00000 | 50.00000 | 32.00000 | 5.00000 | 25.00000 | 53.00000 | 29.00000 | 45.00000 | 13.00000 | 53.00000 | 92.00000 | 107.00000 | 12.00000 | 26.00000 | 51.00000 | 74.00000 | 86.00000 | 14.00000 | 24.00000 | 111.00000 | 17.00000 | 9.00000 | 105.00000 | 19.00000 | 25.00000 | 13.00000 | 7.00000 | 14.00000 | 13.00000 | 15.00000 | 57.00000 | 6.00000 | 35.00000 | 14.00000 | 44.00000 | 13.00000 | 8.00000 | 51.00000 | 19.00000 | 16.00000 | 63.00000 | 52.00000 | 111.00000 | 55.00000 | 56.00000 | 78.00000 | 5.00000 | 57.00000 | 52.00000 | 42.00000 | 11.00000 | 46.00000 | 5.00000 | 17.00000 | 115.00000 | 89.00000 | 17.00000 | 16.00000 | 58.00000 | 47.00000 | 90.00000 | ... | 8.00000 | 17.00000 | 28.00000 | 48.00000 | 38.00000 | 115.00000 | 24.00000 | 41.00000 | 89.00000 | 56.00000 | 34.00000 | 18.00000 | 71.00000 | 31.00000 | 39.00000 | 64.00000 | 116.00000 | 94.00000 | 48.00000 | 66.00000 | 25.00000 | 64.00000 | 47.00000 | 63.00000 | 7.00000 | 23.00000 | 86.00000 | 38.00000 | 67.00000 | 11.00000 | 116.00000 | 100.00000 | 60.00000 | 53.00000 | 48.00000 | 112.00000 | 49.00000 | 26.00000 | 94.00000 | 37.00000 | 116.00000 | 51.00000 | 62.00000 | 91.00000 | 90.00000 | 91.00000 | 17.00000 | 14.00000 | 12.00000 | 63.00000 | 63.00000 | 16.00000 | 34.00000 | 20.00000 | 76.00000 | 56.00000 | 114.00000 | 4.00000 | 4.00000 | 98.00000 | 5.00000 | 28.00000 | 41.00000 | 3.00000 | 3.00000 | 3.00000 | 3.00000 | 2.00000 | 3.00000 | 104.00000 | 27.00000 | 5.00000 | 63.00000 | 46.00000 | 5.00000 | 1.00000 | 1.00000 | 2.00000 | 1.00000 | 2.00000 | 2.00000 | 61.00000 | 87.00000 | 3.00000 | 11.00000 | 3.00000 | 6.00000 | 67.00000 | 105.00000 | 44.00000 | 64.00000 | 104.00000 | 22.00000 | 56.00000 | 34.00000 | 26.00000 | 12.00000 | 42.00000 | 27.00000 | 78.00000 |
| Proportion_Electricity | 0.52798 | 0.46747 | 0.60329 | 0.42855 | 0.63926 | 0.58221 | 0.43054 | 0.23725 | 0.95827 | 0.51141 | 0.65919 | 0.50441 | 0.22825 | 0.50248 | 0.58099 | 0.46801 | 1.00000 | 0.10326 | 0.30766 | 0.52426 | 0.33933 | 0.44020 | 0.77429 | 0.70974 | 0.57351 | 0.38052 | 0.74868 | 1.00000 | 0.33971 | 0.87807 | 0.25668 | 0.62655 | 0.53347 | 0.53840 | 0.66032 | 0.17714 | 0.45494 | 0.62233 | 0.35414 | 0.74783 | 0.50352 | 0.68401 | 0.93759 | 0.56500 | 0.71442 | 1.00000 | 0.85041 | 0.55724 | 0.38717 | 0.54936 | 0.30481 | 1.00000 | 0.93605 | 0.46418 | 1.00000 | 0.47475 | 0.37230 | 1.00000 | 0.68164 | 0.74877 | 0.69218 | 0.70246 | 0.56956 | 0.70036 | 0.85196 | 0.56725 | 0.67782 | 0.77539 | 0.60325 | 0.85178 | 0.03216 | 0.63231 | 0.54759 | 0.67969 | 0.57356 | 0.80644 | 0.44221 | 0.58680 | 0.85540 | 0.25951 | 0.38313 | 0.49116 | 0.35798 | 0.34127 | 0.20910 | 0.67159 | 0.44587 | 0.43942 | 0.48916 | 0.70427 | 0.42907 | 0.85811 | 0.45131 | 0.49658 | 1.00000 | 0.51663 | 0.74725 | 0.75602 | 0.41283 | 0.12420 | ... | 0.62967 | 0.32725 | 1.00000 | 0.51037 | 0.18667 | 0.34827 | 0.47570 | 0.34495 | 1.00000 | 0.72680 | 1.00000 | 0.69649 | 0.47470 | 1.00000 | 0.63491 | 0.64218 | 1.00000 | 0.33007 | 1.00000 | 0.16566 | 0.73350 | 1.00000 | 0.76660 | 0.91447 | 0.57521 | 0.59828 | 0.40145 | 0.58394 | 1.00000 | 0.45791 | 1.00000 | 1.00000 | 0.21895 | 0.31834 | 0.56424 | 0.42396 | 0.34999 | 0.49858 | 0.89702 | 0.89693 | 1.00000 | 0.71119 | 0.46966 | 0.68433 | 0.25465 | 0.36489 | 1.00000 | 0.46391 | 1.00000 | 0.24815 | 0.39910 | 1.00000 | 1.00000 | 0.81070 | 0.59282 | 0.82777 | 1.00000 | 0.97944 | 0.67655 | 0.34575 | 0.63089 | 0.91302 | 1.00000 | 1.00000 | 0.97701 | 1.00000 | 0.61924 | 0.95146 | 0.33130 | 0.87094 | 0.68969 | 0.69511 | 0.55760 | 1.00000 | 0.80373 | 0.55063 | 0.90365 | 0.90039 | 0.83815 | 0.74973 | 0.68112 | 0.20323 | 0.49021 | 0.64995 | 1.00000 | 0.40807 | 0.51472 | 0.66876 | 0.57882 | 0.23551 | 1.00000 | 0.21098 | 0.42431 | 0.71565 | 0.67844 | 0.61697 | 0.41730 | 0.31082 | 0.48490 | 0.37519 |
| Proportion_NaturalGas | 0.18225 | 0.53253 | 0.39671 | 0.57145 | 0.04903 | 0.41779 | 0.32330 | 0.47083 | 0.00000 | 0.06327 | 0.34081 | 0.26097 | 0.36316 | 0.49752 | 0.41901 | 0.53199 | 0.00000 | 0.89674 | 0.69234 | 0.47574 | 0.66067 | 0.55980 | 0.22571 | 0.29026 | 0.42649 | 0.61948 | 0.25132 | 0.00000 | 0.66029 | 0.12193 | 0.74332 | 0.37345 | 0.46653 | 0.46160 | 0.33968 | 0.82286 | 0.54506 | 0.37767 | 0.64586 | 0.25217 | 0.49648 | 0.00000 | 0.06241 | 0.43500 | 0.00000 | 0.00000 | 0.14959 | 0.44276 | 0.61283 | 0.45064 | 0.69519 | 0.00000 | 0.06395 | 0.53582 | 0.00000 | 0.52525 | 0.62770 | 0.00000 | 0.31836 | 0.03082 | 0.30782 | 0.29754 | 0.43044 | 0.29964 | 0.14804 | 0.43275 | 0.32218 | 0.22461 | 0.39675 | 0.14822 | 0.96784 | 0.36769 | 0.45241 | 0.03297 | 0.42644 | 0.19356 | 0.55779 | 0.41320 | 0.14460 | 0.74049 | 0.61687 | 0.50884 | 0.64202 | 0.65873 | 0.79090 | 0.32841 | 0.55413 | 0.56058 | 0.51084 | 0.29573 | 0.57093 | 0.14189 | 0.54869 | 0.50342 | 0.00000 | 0.48337 | 0.25275 | 0.24398 | 0.58717 | 0.87580 | ... | 0.37033 | 0.67275 | 0.00000 | 0.48963 | 0.81333 | 0.65173 | 0.52430 | 0.65505 | 0.00000 | 0.27320 | 0.00000 | 0.30351 | 0.00000 | 0.00000 | 0.36509 | 0.35782 | 0.00000 | 0.66993 | 0.00000 | 0.83434 | 0.26650 | 0.00000 | 0.23340 | 0.08553 | 0.42479 | 0.40172 | 0.59855 | 0.41606 | 0.00000 | 0.54209 | 0.00000 | 0.00000 | 0.78105 | 0.68166 | 0.43576 | 0.57604 | 0.65001 | 0.50142 | 0.10298 | 0.10307 | 0.00000 | 0.28881 | 0.53034 | 0.31567 | 0.74535 | 0.63511 | 0.00000 | 0.53609 | 0.00000 | 0.75185 | 0.60090 | 0.00000 | 0.00000 | 0.18930 | 0.40718 | 0.17223 | 0.00000 | 0.02056 | 0.32345 | 0.65425 | 0.36911 | 0.08698 | 0.00000 | 0.00000 | 0.02299 | 0.00000 | 0.38076 | 0.04854 | 0.66870 | 0.12906 | 0.31031 | 0.30489 | 0.44240 | 0.00000 | 0.19627 | 0.44937 | 0.09635 | 0.09961 | 0.16185 | 0.25027 | 0.31888 | 0.79677 | 0.50979 | 0.35005 | 0.00000 | 0.59193 | 0.48528 | 0.33124 | 0.42118 | 0.76449 | 0.00000 | 0.78902 | 0.57569 | 0.28435 | 0.32156 | 0.38303 | 0.58270 | 0.68918 | 0.51510 | 0.62481 |
| Proportion_Steam | 0.28977 | 0.00000 | 0.00000 | 0.00000 | 0.31171 | 0.00000 | 0.24616 | 0.29192 | 0.04173 | 0.42532 | 0.00000 | 0.23461 | 0.40859 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.31599 | 0.00000 | 0.00000 | 0.25050 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.22042 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.28734 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.52530 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| TotalGHGEmissions | 249.43000 | 263.51000 | 304.62000 | 208.46000 | 199.99000 | 331.61000 | 576.63000 | 334.87000 | 166.84000 | 411.09000 | 755.12000 | 265.50000 | 411.22000 | 1596.08000 | 102.08000 | 308.60000 | 9.68000 | 249.43000 | 244.51000 | 125.57000 | 149.39000 | 243.37000 | 28.78000 | 123.08000 | 56.97000 | 251.72000 | 39.87000 | 64.23000 | 583.41000 | 77.80000 | 209.92000 | 120.73000 | 191.58000 | 170.01000 | 156.59000 | 139.62000 | 169.49000 | 85.51000 | 602.00000 | 74.04000 | 563.78000 | 444.42000 | 131.03000 | 509.18000 | 123.28000 | 30.19000 | 45.83000 | 138.68000 | 196.34000 | 63.95000 | 229.98000 | 30.95000 | 15.10000 | 97.38000 | 44.92000 | 139.24000 | 138.87000 | 14.17000 | 150.23000 | 338.23000 | 220.94000 | 42.99000 | 208.03000 | 181.93000 | 224.13000 | 366.61000 | 53.45000 | 39.29000 | 82.55000 | 59.61000 | 104.17000 | 105.32000 | 123.02000 | 398.56000 | 49.08000 | 53.27000 | 249.01000 | 80.49000 | 119.09000 | 282.34000 | 177.10000 | 52.94000 | 294.12000 | 212.02000 | 320.35000 | 271.32000 | 62.20000 | 178.50000 | 53.61000 | 42.80000 | 39.54000 | 56.96000 | 289.43000 | 221.62000 | 4.99000 | 314.26000 | 44.06000 | 166.73000 | 206.30000 | 138.96000 | ... | 220.26000 | 77.76000 | 13.75000 | 73.26000 | 32.77000 | 64.88000 | 215.56000 | 29.24000 | 1.68000 | 42.87000 | 8.44000 | 82.57000 | 1110.07000 | 2.86000 | 2.69000 | 38.57000 | 37.51000 | 54.24000 | 8.07000 | 83.30000 | 405.05000 | 2.82000 | 18.87000 | 14.17000 | 292.80000 | 77.23000 | 14.37000 | 97.27000 | 5.86000 | 223.36000 | 3.51000 | 2.35000 | 37.28000 | 792.60000 | 22.38000 | 121.63000 | 717.93000 | 58.52000 | 12.57000 | 48.13000 | 4.76000 | 28.46000 | 20.96000 | 30.22000 | 65.95000 | 55.23000 | 39.40000 | 21.87000 | 7.12000 | 68.39000 | 43.97000 | 26.37000 | 21.05000 | 83.45000 | 391.70000 | 32.72000 | 9.46000 | 92.07000 | 191.97000 | 144.41000 | 352.77000 | 147.84000 | 290.57000 | 9.50000 | 20.10000 | 24.38000 | 184.81000 | 42.20000 | 457.51000 | 29.40000 | 58.28000 | 103.65000 | 437.20000 | 48.53000 | 62.41000 | 65.01000 | 214.19000 | 147.14000 | 202.12000 | 245.16000 | 131.02000 | 157.70000 | 134.80000 | 67.65000 | 4.44000 | 29.18000 | 23.00000 | 14.37000 | 24.73000 | 216.18000 | 3.50000 | 259.22000 | 60.81000 | 7.79000 | 20.33000 | 20.94000 | 32.17000 | 223.54000 | 22.11000 | 41.27000 |
| SiteEnergyUse | 6981428.00000 | 8354235.00000 | 12051984.00000 | 6252842.00000 | 6426022.00000 | 12633744.00000 | 14719853.00000 | 6807227.00000 | 16849234.00000 | 10340282.00000 | 33270518.00000 | 7481768.00000 | 7845112.00000 | 53332648.00000 | 3880806.00000 | 9791557.00000 | 1388717.00000 | 5159342.00000 | 6283226.00000 | 4341706.00000 | 3988729.00000 | 7419765.00000 | 1655612.00000 | 6043853.00000 | 2137686.00000 | 7080118.00000 | 2147062.00000 | 9213841.00000 | 15583947.00000 | 6175812.00000 | 5086863.00000 | 4988241.00000 | 6722886.00000 | 6014078.00000 | 6915293.00000 | 3106930.00000 | 5276777.00000 | 3504957.00000 | 16371764.00000 | 3979132.00000 | 18868768.00000 | 15240688.00000 | 13300859.00000 | 18829136.00000 | 5069854.00000 | 4329966.00000 | 3303553.00000 | 5061274.00000 | 5570472.00000 | 2303288.00000 | 5889882.00000 | 4439034.00000 | 1521329.00000 | 3072599.00000 | 6443260.00000 | 4461969.00000 | 3864699.00000 | 2033074.00000 | 6935723.00000 | 14169301.00000 | 10434387.00000 | 2076630.00000 | 7753080.00000 | 8747819.00000 | 16238985.00000 | 13609040.00000 | 2447651.00000 | 2266570.00000 | 3265995.00000 | 4316654.00000 | 2017694.00000 | 4399828.00000 | 4418055.00000 | 13901935.00000 | 1841968.00000 | 3349666.00000 | 7613116.00000 | 3091559.00000 | 8728584.00000 | 6863464.00000 | 4998015.00000 | 1738724.00000 | 8037287.00000 | 5674450.00000 | 7370743.00000 | 12263687.00000 | 1911611.00000 | 5436057.00000 | 1755264.00000 | 2075833.00000 | 1186800.00000 | 4213726.00000 | 8964061.00000 | 7338511.00000 | 715446.00000 | 10735331.00000 | 2364351.00000 | 9146727.00000 | 6056481.00000 | 2933003.00000 | ... | 9155568.00000 | 2045592.00000 | 1972078.62500 | 2478045.50000 | 736428.62500 | 1751483.87500 | 6917280.00000 | 786253.68750 | 241478.59380 | 2189735.00000 | 1211349.25000 | 3936421.50000 | 25311526.00000 | 409578.40630 | 113130.00000 | 1642471.50000 | 5381105.00000 | 1431784.00000 | 1157976.50000 | 1832031.50000 | 21022288.00000 | 405050.40630 | 1063960.12500 | 1298039.87500 | 11019641.00000 | 3027867.50000 | 415364.59380 | 3717064.00000 | 840549.37500 | 6983736.00000 | 503744.68750 | 336745.40630 | 866806.00000 | 20628680.00000 | 826488.68750 | 3625407.50000 | 19423344.00000 | 1943899.25000 | 1072151.37500 | 4103800.75000 | 683274.00000 | 1402048.50000 | 666687.50000 | 1403094.75000 | 1594504.87500 | 1522596.75000 | 5652231.00000 | 689803.00000 | 1021754.68800 | 1641667.00000 | 1267348.50000 | 3782378.25000 | 3020001.50000 | 5313217.00000 | 15206762.00000 | 2193115.00000 | 1357119.87500 | 11624548.00000 | 8767617.00000 | 3886545.00000 | 14697487.00000 | 13458948.00000 | 41680640.00000 | 1363191.62500 | 2502131.00000 | 3497215.50000 | 7531325.00000 | 4581493.00000 | 12095818.00000 | 2274296.75000 | 2737534.50000 | 4926695.00000 | 15965859.00000 | 6960904.50000 | 3894142.50000 | 2346692.50000 | 18760766.00000 | 12720069.00000 | 13998638.00000 | 13239050.00000 | 6042399.50000 | 3605855.50000 | 4420650.50000 | 2925780.00000 | 636965.50000 | 851053.81250 | 783423.00000 | 645665.37500 | 936616.50000 | 5117308.50000 | 502667.68750 | 5976246.00000 | 1813404.00000 | 387810.00000 | 932082.12500 | 849745.68750 | 950276.18750 | 5765898.00000 | 719471.18750 | 1152896.25000 |
| NbListOfAllPropertyUseTypes | 1.00000 | 3.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 3.00000 | 3.00000 | 2.00000 | 5.00000 | 0.00000 | 1.00000 | 2.00000 | 1.00000 | 1.00000 | 4.00000 | 1.00000 | 1.00000 | 4.00000 | 1.00000 | 1.00000 | 2.00000 | 2.00000 | 1.00000 | 3.00000 | 3.00000 | 3.00000 | 3.00000 | 3.00000 | 0.00000 | 2.00000 | 2.00000 | 1.00000 | 1.00000 | 2.00000 | 1.00000 | 1.00000 | 2.00000 | 1.00000 | 6.00000 | 4.00000 | 2.00000 | 1.00000 | 5.00000 | 3.00000 | 2.00000 | 2.00000 | 1.00000 | 1.00000 | 2.00000 | 1.00000 | 1.00000 | 1.00000 | 2.00000 | 2.00000 | 1.00000 | 2.00000 | 1.00000 | 2.00000 | 1.00000 | 2.00000 | 1.00000 | 2.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 2.00000 | 3.00000 | 1.00000 | 1.00000 | 2.00000 | 1.00000 | 3.00000 | 1.00000 | 3.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 3.00000 | 1.00000 | 4.00000 | 1.00000 | 1.00000 | 5.00000 | 1.00000 | 1.00000 | ... | 4.00000 | 2.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 2.00000 | 1.00000 | 2.00000 | 3.00000 | 1.00000 | 2.00000 | 2.00000 | 2.00000 | 1.00000 | 1.00000 | 3.00000 | 1.00000 | 1.00000 | 2.00000 | 2.00000 | 1.00000 | 2.00000 | 1.00000 | 1.00000 | 1.00000 | 2.00000 | 2.00000 | 1.00000 | 4.00000 | 1.00000 | 1.00000 | 2.00000 | 3.00000 | 2.00000 | 1.00000 | 1.00000 | 1.00000 | 2.00000 | 4.00000 | 1.00000 | 2.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 4.00000 | 1.00000 | 3.00000 | 1.00000 | 1.00000 | 2.00000 | 1.00000 | 1.00000 | 2.00000 | 1.00000 | 3.00000 | 1.00000 | 2.00000 | 3.00000 | 1.00000 | 5.00000 | 2.00000 | 2.00000 | 1.00000 | 3.00000 | 2.00000 | 2.00000 | 7.00000 | 3.00000 | 4.00000 | 2.00000 | 1.00000 | 1.00000 | 1.00000 | 5.00000 | 3.00000 | 2.00000 | 2.00000 | 3.00000 | 3.00000 | 1.00000 | 1.00000 | 1.00000 | 5.00000 | 1.00000 | 1.00000 | 1.00000 | 5.00000 | 4.00000 | 1.00000 | 1.00000 | 4.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 3.00000 | 5.00000 | 5.00000 |
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| LargestPropertyUseType_College/University | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Distribution Center | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Hotel | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | ... | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_K-12 School | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | ... | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Laboratory | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Library | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Manufacturing/Industrial Plant | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Medical Office | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Multifamily Housing | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Museum | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Non-Refrigerated Warehouse | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Office | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Other | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Other - Education | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Other - Entertainment/Public Assembly | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Other - Lodging/Residential | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Other - Mall | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Other - Recreation | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 |
| LargestPropertyUseType_Other - Services | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Other/Specialty Hospital | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Parking | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Refrigerated Warehouse | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Repair Services (Vehicle, Shoe, Locksmith, etc) | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Residence Hall/Dormitory | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Restaurant | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Retail Store | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Self-Storage Facility | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Senior Care Community | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Social/Meeting Hall | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Strip Mall | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Supermarket/Grocery Store | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Urgent Care/Clinic/Other Outpatient | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| LargestPropertyUseType_Worship Facility | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Aucun ou Non Renseigné | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | ... | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Bank Branch | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Bar/Nightclub | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Data Center | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Distribution Center | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Fitness Center/Health Club/Gym | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 |
| SecondLargestPropertyUseType_K-12 School | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Laboratory | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Manufacturing/Industrial Plant | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Medical Office | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Movie Theater | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Multifamily Housing | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Non-Refrigerated Warehouse | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Office | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Other | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Other - Entertainment/Public Assembly | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Other - Services | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Parking | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Repair Services (Vehicle, Shoe, Locksmith, etc) | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Restaurant | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Retail Store | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Self-Storage Facility | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| SecondLargestPropertyUseType_Supermarket/Grocery Store | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Aucun ou Non Renseigné | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | ... | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Bank Branch | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Data Center | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Financial Office | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Fitness Center/Health Club/Gym | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Food Service | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Laboratory | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Medical Office | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Multifamily Housing | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Non-Refrigerated Warehouse | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Office | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Other | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Other - Entertainment/Public Assembly | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ThirdLargestPropertyUseType_Other - Recreation | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
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| ZipCode_98144 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ZipCode_98199 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ... | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
190 rows × 1425 columns
print('Nouvelles dimensions de \'df\': ',df.shape)
Nouvelles dimensions de 'df': (1425, 190)
Avant d’appliquer une transformation à nos variables continues,
il est important de choisir la transformation la plus adéquate à chaque variable.
Ce choix se fera en fonction de la distribution de chaque variable.
Afin de mieux comprendre nos données, nous allons visualiser leur distribution.
Nous cherchons, idéalement, à obtenir une distribution de type Normale.
Nous allons donc successivement :
Rappel des données continues à analyser et à transformer :
dictVarCatCont['Ordre_Distance']
['ageBuilding', 'NumberofFloors', 'PropertyGFATotal', 'PropertyGFAParking', 'PropertyGFABuilding', 'LargestPropertyUseTypeGFA', 'SecondLargestPropertyUseTypeGFA', 'ThirdLargestPropertyUseTypeGFA', 'ENERGYSTARScore', 'Latitude', 'Longitude', 'Proportion_Electricity', 'Proportion_NaturalGas', 'Proportion_Steam', 'TotalGHGEmissions', 'SiteEnergyUse', 'NbListOfAllPropertyUseTypes']
for i in dictVarCatCont['Ordre_Distance']:
print(i)
plt.figure(figsize=(7,7))
sns.distplot(df[i])
ageBuilding NumberofFloors PropertyGFATotal PropertyGFAParking PropertyGFABuilding
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
LargestPropertyUseTypeGFA SecondLargestPropertyUseTypeGFA ThirdLargestPropertyUseTypeGFA ENERGYSTARScore Latitude
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
Longitude Proportion_Electricity Proportion_NaturalGas Proportion_Steam TotalGHGEmissions SiteEnergyUse
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
NbListOfAllPropertyUseTypes
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
Les échelles de données sont très disparates.
Les distributions ne ressemblent pas à une distribution Normale
à l'exception des colonnes 'Latitude' et 'Longitude'.
Les colonnes renseignant sur une superficie sont unimodales non centrées
Les autres colonnes sont multimodales.
Création d'une copie du DataFrame **df** pour tester les transformations
sans conséquence sur le DataFrame principal :
df2 = df.copy()
ss = StandardScaler()
df2[dictVarCatCont['Ordre_Distance']] = ss.fit_transform(df2[dictVarCatCont['Ordre_Distance']])
Aperçu des variables Standardisées :
df2[dictVarCatCont['Ordre_Distance']].head().T
| 0 | 1 | 5 | 6 | 7 | |
|---|---|---|---|---|---|
| ageBuilding | 1.02072 | -1.04104 | -1.13069 | 1.05060 | 1.05060 |
| NumberofFloors | 1.91057 | 1.68081 | -0.38695 | 1.68081 | 0.99156 |
| PropertyGFATotal | -0.01987 | 0.11223 | 0.05742 | -0.06724 | 0.10521 |
| PropertyGFAParking | -0.33818 | 0.07147 | 0.67339 | -0.33818 | -0.33818 |
| PropertyGFABuilding | 0.10961 | 0.11034 | -0.19619 | 0.05107 | 0.26418 |
| LargestPropertyUseTypeGFA | 0.23406 | 0.17862 | 0.23888 | 0.14785 | 0.40848 |
| SecondLargestPropertyUseTypeGFA | -0.43571 | 0.00682 | -0.43571 | -0.43571 | -0.43571 |
| ThirdLargestPropertyUseTypeGFA | -0.21892 | 0.21494 | -0.21892 | -0.21892 | -0.21892 |
| ENERGYSTARScore | 0.02890 | -0.46177 | nan | -1.37301 | nan |
| Latitude | -0.08685 | -0.06734 | 0.00370 | -0.04585 | -0.07168 |
| Longitude | -0.19017 | 0.00042 | -0.13951 | 0.02575 | 0.08455 |
| Proportion_Electricity | -0.71328 | -0.94854 | -0.42053 | -1.09984 | -0.28069 |
| Proportion_NaturalGas | -0.35141 | 1.00015 | 0.47609 | 1.15032 | -0.86544 |
| Proportion_Steam | 3.92445 | -0.21508 | -0.21508 | -0.21508 | 4.23792 |
| TotalGHGEmissions | 0.85790 | 0.94236 | 1.18895 | 0.61215 | 0.56135 |
| SiteEnergyUse | 0.19347 | 0.36530 | 0.82813 | 0.10228 | 0.12395 |
| NbListOfAllPropertyUseTypes | -0.78152 | 0.94643 | -0.78152 | -0.78152 | -0.78152 |
for i in dictVarCatCont['Ordre_Distance']:
print(i)
plt.figure(figsize=(7,7))
sns.distplot(df2[i])
ageBuilding NumberofFloors PropertyGFATotal PropertyGFAParking PropertyGFABuilding LargestPropertyUseTypeGFA
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
SecondLargestPropertyUseTypeGFA ThirdLargestPropertyUseTypeGFA ENERGYSTARScore Latitude Longitude Proportion_Electricity
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
Proportion_NaturalGas Proportion_Steam TotalGHGEmissions SiteEnergyUse
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
NbListOfAllPropertyUseTypes
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
Seules les variables 'Latitude' et 'Longitude' ont une distribution qui ressemble à une distribution Normale.
Je n'effectuerai pas de transformation Box-Cox sur ces deux variables.
La transformation Box-Cox ne peut s'appliquer que sur des Series contenant des valeurs strictement positives.
Pour chaque variable continue de mon DataFrame, je testerai préalablement à chaque application
de la transformation s'il existe des valeurs égales à 0.
(Seule la colonne 'Longitude' contient des valeurs négatives)
Si tel est le cas, la valeur sera remplacée par une valeur très proche de 0 : 0.00001
for col in dictVarCatCont['Ordre_Distance']:
if any(df[col] < 0):
print('Warning - valeurs négatives :',col)
Warning - valeurs négatives : Longitude
df2 = df.copy()
for col in [i for i in dictVarCatCont['Ordre_Distance'] if i not in ['Latitude', 'Longitude']]:
if any(df2[col] == 0):
df2.loc[df2[col] == 0,col] = 0.00001
df2[col],_ = stats.boxcox(df2[col])
plt.figure(figsize=(7,7))
sns.distplot(df2[col])
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
Après analyse des différentes distributions après transformation via **Standard Scaler**
et **Box-Cox** sur mes variables contenant des données continues,
je répartis les transformations finales de cette façon :
Je supprime également les variables que je juge non pertinentes au regard de leur distribution
Transformation **StandardScaler** des variables sélectionnées :
ss = StandardScaler()
df[['ageBuilding',
'ENERGYSTARScore',
'Latitude',
'Longitude',
'Proportion_Electricity',
'Proportion_NaturalGas',
'Proportion_Steam']] = ss.fit_transform(df[['ageBuilding',
'ENERGYSTARScore',
'Latitude',
'Longitude',
'Proportion_Electricity',
'Proportion_NaturalGas',
'Proportion_Steam']])
Transformation **BoxCox** des variables sélectionnées :
for col in ['NumberofFloors',
'PropertyGFATotal',
'PropertyGFABuilding',
'LargestPropertyUseTypeGFA',
'TotalGHGEmissions',
'SiteEnergyUse']:
if any(df[col] == 0): # Si des valeurs null existent dans la Series, on les remplace par une valeur très proche de 0
df.loc[df[col] == 0,col] = 0.00001
df[col],_ = stats.boxcox(df[col])
Suppression des variables sélectionnées :
df.drop(['PropertyGFAParking',
'SecondLargestPropertyUseTypeGFA',
'ThirdLargestPropertyUseTypeGFA',
'NbListOfAllPropertyUseTypes'],
axis=1,
inplace=True)
Mise à jour des variables continues dans le dictionnaire **dictVarCatCont** :
dictVarCatCont['Ordre_Distance'] = [i for i in dictVarCatCont['Ordre_Distance'] if \
i not in ['PropertyGFAParking',
'SecondLargestPropertyUseTypeGFA',
'ThirdLargestPropertyUseTypeGFA',
'NbListOfAllPropertyUseTypes']]
for col in dictVarCatCont['Ordre_Distance']:
plt.figure(figsize=(10,10))
sns.distplot(df[col])
C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning) C:\Users\waldu\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
Qu'appelle-t-on corrélation ?
En probabilité, la corrélation entre plusieurs variables est
une notion liée à leur dépendance, ou indépendance les unes aux autres.
Cette corélation est très souvent réduite à la corrélation
linéaire entre variables quantitatives. C'est ce que nous ferons ici.
Nous utiliserons la fonction corr() de Pandas
pour calculer les coefficients de Pearson.
Le coefficient de Pearson est un indice reflétant
une relation linéaire entre deux variables continues.
Le coefficient de corrélation varie entre -1 et +1,
0 reflétant une relation nulle entre les deux variables,
une valeur négative (corrélation négative) signifiant que
lorsqu'une des variables augmente, l'autre diminue ;
tandis qu'une valeur positive (corrélation positive) indique
que les deux variables varient ensemble dans le même sens.
Je vais dans cette partie analyser les rapports de corrélation
qui existe entre les différentes variables de notre DataFrame 'df'.
Je vais diviser mon étude en 2 parties :
Les modèles de Machine Learning fonctionnent mieux à prédire nos données
à partir de caractéristiques (features) corrélées à nos Targets.
Nous allons identifier et mettre en évidence les variables les plus corrélées,
les plus importantes et donc les plus aptes à aider correctement
les algorithmes de Machine Learning à effectuer de bonne prédiction
dans le problème qui nous est posé.
J'affiche ci-dessous la *heatmap de corrélation*,
avec les colonnes filtrées sur les Targets pour plus de lisibilité :
plt.figure(figsize=(5,15))
sns.heatmap(df[dictVarCatCont['Ordre_Distance']].corr()[['TotalGHGEmissions','SiteEnergyUse']],
annot=True,
fmt='.2f',
linewidths=0.5,
square=True)
<AxesSubplot:>
A l'inverse de la corrélation variable/target,
une corrélation trop forte entre deux variables du jeu de données
peut nuire aux performances de notre modèle de Machine Learning.
J'affiche ci-dessous la heatmap de corrélation entre les variables quantitatives :
plt.figure(figsize=(15,15))
sns.heatmap(df[dictVarCatCont['Ordre_Distance']].drop(['TotalGHGEmissions','SiteEnergyUse'], axis=1).corr(),
annot=True,
fmt='.2f',
linewidths=0.5,
square=True)
<AxesSubplot:>
Maintenant que nous avons mieux compris les relations qui existent
entre nos variables et nos Targets, ainsi que nos variables entre-elles
nous allons voir comment faire de la sélection de variables.
Cette technique consiste, parmi toutes les variables disponibles dans un Dataset,
à trouver puis à sélectionner celles qui seront les plus utiles au développement
d’un modèle de Machine Learning.
En effet, lorsqu'on fournit trop de variables à un modèle de Machine Learning,
et notamment des variables qui ne sont pas utiles au modèle,
alors cela impactera ses performances de façon négative.
Plusieurs techniques existent pour faire de la sélection de variables.
Dans ce projet, j’effectuerai ma sélection de variables avec l’algorithme Lasso
en utilisant le module LassoCV de Sklearn et en expliquant en détail chaque étape.
J’effectuerai deux sélections de variables différentes, une pour chacune de nos deux Targets.
Je sauvegarde préalablement notre DataFrame '**df**' dans un objet Pickle :
pickle.dump(df, open('dfBeforeFeatureSelection', 'wb'))
# df = pickle.load(open('dfBeforeFeatureSelection', 'rb'))
Avant d'appliquer le modèle LassoCV, je vais préalablement séparer nos données en deux groupes.
Les features de notre jeu de données, d'un côté, seront stockées dans la variable X
et notre target 'TotalGHGEmissions' sera stockée dans la variable y.
Je retirerai également la variable 'ENERGYSTARScore' dans cette étape.
Je séparerai ensuite c'est deux variables en deux groupes :
Les données seront séparées de cette manière dans les variables :
Pour optimiser les performances de l'algorithme Lasso,
j'appliquerai la technique de Cross Validation.
Cette technique consiste à entraîner puis valider notre modèle
sur plusieurs découpes possibles du train-set.
Ainsi le score obtenu sera celui de la moyenne des différentes
découpes possibles du train-set.
Ainsi, en découpant le train-set en 5 parties, on pourra entrainer
notre modèle sur les 4 premières parties puis le valider sur la cinquième partie.
Ensuite l'algorithme refera la même chose pour toutes les configurations possibles.
LassoCV :
Création de nos variables X (features) et y (target) :
X = df.drop(['TotalGHGEmissions','SiteEnergyUse','ENERGYSTARScore'], axis=1)
y = df.TotalGHGEmissions
Création de nos jeux de train et de test,
avec un random_state défini pour la reproductivité des résultats :
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=RS) # Pour la reproductibilité des résultats
Nous allons maintenant itérer notre modèle avec
plusieurs valeurs possibles d'alpha.
Je définis une gamme assez large d'alphas allant
de 10e-4 à 10e1 découpés en 400 échantillons.
A chaque itération, nous allons enregistrer dans une List :
alphas = np.logspace(-4, 1, 300)
coefs=[]
errors = []
for alpha in alphas:
lcv = LassoCV(alphas=[alpha],
fit_intercept=False,
normalize=False,
cv=5,
max_iter=10000,
random_state=RS)
lcv.fit(X_train, y_train)
coefs.append(lcv.coef_)
errors.append(mean_squared_error(y_test,lcv.predict(X_test)))
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.44441503215035, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 376.3075881905988, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398.6848584489616, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.1978464592352, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.70672739718145, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 510.70825663623265, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.6498391586622, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 377.4553864974731, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.85078345307284, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 390.57652404794476, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 404.8901880077558, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 512.2531051996292, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394.8633871400631, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 378.6065038567865, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.0174495971169, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 391.9716031562387, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.0799974786744, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 513.8094758971081, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.0833144252467, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 379.7586044851978, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.1864838210399, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.35513480049167, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.2733429854334, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 515.3738787130073, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 397.30906844042187, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 380.9058344097945, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.3538492962649, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394.777132283048, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408.46686331937985, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 516.9418312796878, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398.54003353189273, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 382.05622606245527, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 404.5165488496293, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.21008820981024, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409.6550338576358, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 518.5086399558957, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.7715670155637, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 383.20400711471734, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.6596882104302, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 397.64343233519503, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410.81392165400405, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 520.1220110196093, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400.9377421438452, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 384.33457088457317, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.799695577007, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.10686101343657, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411.97967697079457, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 521.6761146577428, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.1404700795309, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 385.4502202932541, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.9217720579397, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400.5190234650543, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.1277006919688, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 523.2000676519315, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.3285784283098, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 386.5122262620683, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409.0199345027925, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.9428910104125, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.24811345969124, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 524.7044242449696, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 404.4961795293009, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 387.56749619612026, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410.0874323677787, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.3531819358257, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 415.32618324791645, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 526.1764363734704, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.68980025769764, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388.58549589497744, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411.11661201192607, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 404.7034153927089, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.37794179648023, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 527.6067073296867, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.7821115570193, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.5580613384825, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412.1002359375968, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.98258684995903, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 417.3877825808113, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 528.9836071913281, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.84307544367425, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 390.4653834844242, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.0296879430861, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.2360244646094, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 418.34107619673546, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 530.2216657500362, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408.8630326467974, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 391.29998503744497, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.8914780180353, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408.43828177177267, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 419.23261741673355, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 531.4258698671476, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409.83338432969373, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.09869677087966, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.6595979398573, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409.5075565737675, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 420.0616942971899, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 532.5687089567252, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410.7513281746336, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.8161673939092, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 415.4058763166072, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410.5632320068913, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 420.8002629937051, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 533.6073418079077, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411.60229449839863, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.41267649755343, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.0157440894489, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411.5758614114471, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 421.4404307531412, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 534.5102921353815, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412.2609628926712, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.8969530357461, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.42236389171813, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412.5047606047648, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 421.9694807562487, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 535.279010880371, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412.87126157813924, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394.25002343445277, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.6918485783048, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.4178003204598, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422.3633849977693, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 535.8971781993832, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.3560081994119, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394.4556941721566, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.83473371989476, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.103094275689, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422.61124374974816, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 536.3599606934486, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.7004638357913, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394.544410393612, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.8227630152186, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.68848500609283, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422.5075040902738, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 536.5842311708321, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.8897567321014, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394.47003129514115, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.6300595224275, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 415.1226415901075, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422.30762981819, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 536.5873964950788, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.89286855688476, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394.1494832609398, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.1695415571134, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 415.3330609613351, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 421.97920965618385, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 536.2651116812501, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.72126330721915, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.49105028456404, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 415.4375152606142, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 415.3198257593342, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 421.4092410358253, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 535.7105333793986, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.34718892390504, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.65585063033194, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.4998465652073, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.7493505596059, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 420.5652276458324, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 534.827908528214, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412.8968893616725, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 391.43732867850974, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.26796994236855, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.1974234615191, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 419.42031100036303, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 533.5794005301891, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412.06452431433354, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.92221443604984, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411.72036026978327, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.3393652848499, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 417.9403360331223, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 532.0411731687311, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410.8628389239859, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388.03264864146416, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409.79683840142945, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412.1385408234333, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.12233734538745, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 529.9906311408164, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409.3158662856141, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 385.7256929995862, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.42472321583176, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410.5498454301226, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.8464834290716, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 527.3951410570492, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.42673344199557, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 382.365546115156, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 404.54583484946414, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408.6268473376039, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411.0943042066233, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 523.5621984100059, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.32053506803925, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 382.30778214909947, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.3093984412802, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.13090480089505, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409.635488705704, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 519.6858434262134, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.6569602850331, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 381.4902736896359, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.6379512095601, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.1044947059149, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408.8251148766623, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 516.8104685477788, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.8822940922138, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 380.49306592587203, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.5597401000791, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.3740816950881, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408.615144571893, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 515.6829224675371, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.9849239953626, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 379.4146546583616, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.9914313783834, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400.9751402044761, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.77625881081343, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 514.8843982852123, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.9235320630869, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 378.45294842045865, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.09647037172937, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400.8641576206025, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.8725238254709, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 514.0260476839574, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.6769810011083, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 377.22589929412226, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.33439672716327, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400.4301056186443, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.2678454118338, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 512.7865863695415, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.21955012609686, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 375.3546001866591, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398.0172043612321, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.56906581170165, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.2011253778196, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 511.07050061325543, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.54701364296585, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 372.4906699628481, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 395.9828791765742, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398.464607471387, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.41270585763164, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 508.8162255164149, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.6172831130787, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 369.66117398232285, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.3347516045877, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 397.07310978688116, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.87810942447476, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 506.0456162907868, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 404.52323750770347, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 366.26840283746424, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 390.07811682773206, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 395.2585450082489, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.775544917855, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 502.36335855710337, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.09456508074805, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 362.17252608991714, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 385.901969591171, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.7351291317266, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.0279378314187, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 497.964719588904, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.22794679022553, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 357.072500690062, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 381.1302701605539, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.3959702144906, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388.89136831286226, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 492.51059200029414, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398.963030272974, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 351.1779657503772, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 375.2322469366469, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 385.3770571809033, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 383.5797080472135, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 485.93506950853646, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.2689539970158, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 344.0466069609325, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 368.6482625647067, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 381.0452062647115, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 376.804943819442, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 477.98753314341053, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.4701079010944, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 334.8994758487582, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 360.41839222986926, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 375.92974887319605, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 369.263589837366, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 468.7100784644307, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388.637441144024, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 324.5965192986591, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 350.60606929980855, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 369.24486675858964, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 360.24219403436786, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 457.6502365910342, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 385.50953130098355, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 314.6488187198647, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 338.6899502938825, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 361.0381589180814, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 349.5618962430615, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 444.39308587068683, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 383.11889593017105, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 318.1176048013954, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.880413118979, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 351.6425341915961, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 338.62548425193535, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 428.78526962647686, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 381.9057242698582, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.3919168153204, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.41147484020973, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 340.2732752924229, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 335.80069780892467, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 425.4960916280169, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 380.51691116477184, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 324.20014221458155, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 340.79782968891084, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.4400174608092, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 339.22725850533163, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 430.1345403590966, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 379.07241777245207, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 326.85081391388974, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 344.02977628973264, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.60447559593524, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 343.06820537622866, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 434.4140752943561, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 377.0052977551224, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 329.7833575295917, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 347.0176545949416, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 341.47429624728136, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 346.40200235506916, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 438.27702291217133, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 374.53502704607445, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.33421854043314, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 349.63730742198885, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 345.11518862823516, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 349.17609941724993, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 442.1575569462778, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 371.7281469345621, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 334.6708154311522, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 352.0580098654003, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 348.48190506561355, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 351.7454794346894, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 445.5636877049095, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 368.3666015864541, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336.00595333490236, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 353.4358063214432, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 351.7338709282978, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 353.8860030748931, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 448.876619652802, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 364.476581671463, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.88415878913247, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 354.93135761708794, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 354.6185719988534, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 355.9560080970534, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 451.6160620069026, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 359.5857028285359, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 339.50836060660316, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 356.8962674039199, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 357.16968959949213, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 356.90528802246655, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 454.29090374571757, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 353.4608986500865, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 340.82600139822097, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 358.4918725938683, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 359.2792766562058, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 358.2094159117404, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 456.56124876357984, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 346.64561912671286, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 341.78729861904446, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 359.81218217108085, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 361.19157409329256, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 359.3668652504925, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 458.4503879575852, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 339.1389020385605, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 342.4231549504556, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 360.69594086968584, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 362.7293285216109, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 360.1871720835527, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 460.2167206968829, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 329.7461133891535, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 342.542871093487, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 361.2085885259335, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 363.88200855891034, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 360.58207042122183, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 461.18530695885903, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 319.5662907608564, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 342.12078057875067, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 361.2879680114513, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 364.5448638630559, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 360.5222138610745, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 461.49470575754543, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 311.85973749358243, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 314.09371062076804, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 360.91485685214025, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 364.7927015710012, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 359.96673943313726, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 461.20505211376985, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 301.969627110554, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 310.17908862685135, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.7213705782788, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 364.6241894199233, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 327.84292282266506, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 460.20826754974036, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 290.6671358273446, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 305.6906415957504, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.86884030078085, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 363.32897083580053, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 324.4513248308114, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 430.3469426551022, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.4330056666539, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 302.74986724555754, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.71305620859766, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.56191720998737, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.3904427429523, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 419.343130213221, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.0591660088011, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 299.74617496629384, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 317.3835471823039, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 330.32794539171897, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 318.06424995063065, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.95560592352945, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 244.2385927128983, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 296.4374612454524, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 314.07026576887387, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 326.8850850364828, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 314.3747104248848, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409.34683216057584, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249.36436177165092, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 292.8331284957577, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 310.94224100871565, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.75587504735284, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 310.72390334382857, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.9139653039659, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 254.68365162837915, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 289.1371646441437, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 307.0800330061593, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 312.1142720504641, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 306.8764592184627, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 386.89231950158205, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259.6557280397423, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 285.32591445366666, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 303.2425455362113, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 308.7039306567662, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 302.844469213246, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 381.77137100936636, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.2445514300523, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 281.3449979346998, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 298.94776103723734, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 304.8731379775391, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 298.4441112511586, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 376.51309711943867, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 268.60814051602136, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.1672680272043, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 294.6113657327098, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 300.89426058367906, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 293.9618804614535, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 369.72047924388835, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 272.79295308884997, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 272.78108646639225, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 290.1473380641458, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 296.67272076314634, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 289.30008662714175, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 363.86317097457413, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.6531745118735, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 268.3229387455677, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 285.5738366348124, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 292.2611825558029, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 284.4825830386183, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 357.9277502217062, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 280.06640700525577, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.3292199782145, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 280.7985677481136, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 288.5126101874786, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279.2286773076663, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 351.60290416891024, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 283.2917923629632, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.4024302808954, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.7494266628294, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 284.0192467320427, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273.95313136106216, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 344.9912236133882, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 286.26166448593005, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 267.2512280840576, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273.08535952734235, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.7360918161163, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 271.9614183321298, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 338.2698617245565, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 288.82840609391013, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 247.3002598582221, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.28434744206095, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 274.7108754489154, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.3305821308921, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.2960387536182, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 290.8991267297618, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 241.619823560957, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 257.9233730460924, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 280.0214617179973, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255.52293829202677, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 324.0491186733765, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 292.656489034592, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 235.71492179885092, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 250.6145771612551, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.32358740494374, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249.48415376378523, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 316.5391044695579, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 237.91344977780267, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 229.6055590172515, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 244.41962826350994, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255.19608582345137, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 243.2125292487254, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 308.7694580439139, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 230.8603334076205, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 223.44197649552734, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 238.08811629276875, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 248.87921510931534, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 236.7053163339981, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 300.52027509044626, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 224.32709274137437, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 217.49967900854017, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 231.51529159011773, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 242.45640111733883, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 229.70744208945985, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 291.9339621218468, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 217.4346632520403, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211.0869177690048, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 224.92438235013447, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 235.52775181250718, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 222.75060967817706, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 283.35681705821577, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 210.01202876078207, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 203.83338345506058, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 218.2819574994404, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 228.51447123487628, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 215.60659612208315, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 274.3938775443394, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 203.11096195090204, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 196.8369766271319, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211.16079302409366, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 221.36322653569255, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 208.08063979310862, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.0414790037215, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 195.8883348780247, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 189.6208187745715, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 203.59925693018124, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 213.55801815363304, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200.03583690320318, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255.50779890242552, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 188.494729214921, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 182.15485958164925, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 195.92579503710215, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 206.36696664729163, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 192.0987858311554, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246.38159639339176, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180.88765641749524, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 174.44462560820443, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 187.94560269586123, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198.7604916752731, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 183.97989694852822, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 236.53977370853784, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 173.07259588610543, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166.5256819142096, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180.67496449964267, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190.612443112468, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 175.6422223356759, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 226.49807665458377, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165.05843994172403, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 158.4438177189137, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 172.56524361508502, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 182.55211411080484, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 167.16165936160792, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 216.19417149351852, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 156.84763310645712, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 149.9951063324438, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164.22139833376923, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 174.33059512405646, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 158.60545516044067, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 205.54611274174272, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 148.43477791358362, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 141.6313640260953, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 155.76824079204312, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165.92359941060704, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 149.89447450154148, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194.6765681613956, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 139.91414134536308, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 132.85536232231902, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 147.19484299555847, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 157.42399029613125, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 141.08190864766095, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 183.5719856933481, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 131.27594809555717, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124.28033951767657, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 138.58907134604976, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 148.77162959543512, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 131.9701728176292, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 172.87695055522408, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122.5236788951247, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 115.64149181565517, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129.46127395048438, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 139.93360425820754, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122.85501587792533, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 161.96592889433657, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 113.731175082596, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 106.7419735222519, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 120.34274867336819, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 130.634748913335, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 113.47818685454479, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 150.79787406987043, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 104.79556415968523, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 97.95032809793281, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 110.956649649832, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121.53316041971789, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 104.14544857073554, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 139.48939351366937, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 95.83926818479154, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 89.14082187478743, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 102.04696580791563, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 112.39647081647843, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 94.75009262189167, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 128.3010716189408, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86.74422515621154, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 80.34437560712513, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 92.69440585722987, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 101.07150050825067, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 85.3533811947633, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 117.13303129930352, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 78.16712133885585, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 71.5776899709299, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.43921750701224, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 92.32239832444714, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 76.15593168765531, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 105.40152088261505, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 69.23928464947699, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62.89129578234156, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 74.26545936280331, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.36518363535163, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 67.05669007001052, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 94.17478467988974, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60.58455830776302, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54.342559476190445, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65.21407148350534, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 74.51256553651285, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58.04895314209125, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.07934649015465, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52.11674478801979, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45.98885995292869, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.322401424355576, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65.83524641013514, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49.25687717171377, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 72.3869433712382, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 43.89024884263745, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38.17336917412058, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47.540211268496705, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.25805849459175, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40.693360978203714, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61.48966459460689, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 36.03980458532055, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30.659772104014905, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39.090822826358135, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48.944143236294394, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 32.678198136641186, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50.941802918503754, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 28.648278499165144, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23.55684393355591, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 31.060851516883645, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 41.00709462237376, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25.167269108495475, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40.93438807222765, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21.660041940261408, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.929363688745752, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23.009193139925628, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 33.25551170657127, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18.198013215036326, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 31.462336685711307, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.918074734503307, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10.922840445899055, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.09320168342265, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25.895491726832233, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.818215345933595, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22.679662767844093, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.113115320261386, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.700354833462825, tolerance: 2.2324839677328887 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10.124956055069788, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19.109945732094957, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.190819768044321, tolerance: 2.2300627340789543 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.759986973767809, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.052164850114423, tolerance: 2.227027296400932 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.825622821202614, tolerance: 2.1886403671588326 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.846862873693453, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.520481729549374, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.350104879039691, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5151573184186873, tolerance: 2.2203893074793233 model = cd_fast.enet_coordinate_descent_gram(
Graphique illustrant l'erreur quadratique moyenne en fonction de alpha :
Alpha semble atteindre un minimum entre 10e-3 et 10e-2
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, errors)
ax.set_xscale('log')
plt.xlabel('alpha')
plt.ylabel('erreur: MSE')
plt.axis('tight')
plt.show()
Affichons précisément la valeur de alpha qui minimise l'erreur MSE (Mean Squared Error) :
df_alpha = pd.DataFrame({'alpha':alphas,'MSE':errors})
df_alpha[df_alpha.index == df_alpha.MSE.argmin()]
| alpha | MSE | |
|---|---|---|
| 33 | 0.00036 | 1.07143 |
Observons maintenant, pour le meilleur alpha calculé,
les coefficients affectés à chacune de nos variables.
Un coefficient à 0 signifie que le modèle a pénalisé
la variable correspondante au point de l'éliminer.
s = pd.Series(coefs[df_alpha.MSE.argmin()], index=X.columns)
s
NumberofFloors 0.22425 PropertyGFATotal -0.06135 PropertyGFABuilding 0.02854 LargestPropertyUseTypeGFA 9.80533 DefaultData -0.60051 Outlier 0.07676 Latitude -0.25957 Longitude 0.08068 ageBuilding -0.20058 Proportion_Electricity 0.00000 Proportion_NaturalGas 1.14926 Proportion_Steam 0.47583 YearsENERGYSTARCertified_2008 0.20585 YearsENERGYSTARCertified_2009 0.00000 YearsENERGYSTARCertified_2012 0.10141 YearsENERGYSTARCertified_2013 0.03126 YearsENERGYSTARCertified_2014 0.01838 YearsENERGYSTARCertified_2015 0.01811 YearsENERGYSTARCertified_2016 -0.06337 ListOfAllPropertyUseTypes_Data Center 0.85393 ListOfAllPropertyUseTypes_Distribution Center 0.00000 ListOfAllPropertyUseTypes_Hotel -0.32817 ListOfAllPropertyUseTypes_K-12 School -1.14302 ListOfAllPropertyUseTypes_Medical Office -0.00000 ListOfAllPropertyUseTypes_Multifamily Housing 0.13560 ListOfAllPropertyUseTypes_Non-Refrigerated Warehouse -0.34183 ListOfAllPropertyUseTypes_Office -0.75677 ListOfAllPropertyUseTypes_Other 0.30614 ListOfAllPropertyUseTypes_Parking -0.15434 ListOfAllPropertyUseTypes_Restaurant 0.03316 ListOfAllPropertyUseTypes_Retail Store 0.35109 ListOfAllPropertyUseTypes_Supermarket/Grocery Store 2.03983 ListOfAllPropertyUseTypes_Worship Facility 0.10312 Neighborhood_BALLARD 0.00000 Neighborhood_CENTRAL -1.01174 Neighborhood_DELRIDGE -0.01960 Neighborhood_DOWNTOWN -0.28424 Neighborhood_EAST -0.58038 Neighborhood_GREATER DUWAMISH -0.17083 Neighborhood_LAKE UNION -0.04746 Neighborhood_MAGNOLIA / QUEEN ANNE -0.02759 Neighborhood_NORTH -0.35059 Neighborhood_NORTHEAST 0.13817 Neighborhood_NORTHWEST -0.10565 Neighborhood_SOUTHEAST -0.08227 Neighborhood_SOUTHWEST -0.29360 CouncilDistrictCode_1 -18.96415 CouncilDistrictCode_2 -18.96167 CouncilDistrictCode_3 -18.24879 CouncilDistrictCode_4 -18.71554 CouncilDistrictCode_5 -18.53840 CouncilDistrictCode_6 -18.55396 CouncilDistrictCode_7 -18.63336 BuildingType_Campus -10.56615 BuildingType_NonResidential -11.30698 BuildingType_Nonresidential COS -10.93960 BuildingType_SPS-District K-12 -10.21916 ComplianceStatus_Compliant 0.00000 ComplianceStatus_Error - Correct Default Data -0.20602 PrimaryPropertyType_College/University -0.63172 PrimaryPropertyType_Distribution Center -0.00000 PrimaryPropertyType_Hotel 0.00000 PrimaryPropertyType_K-12 School -0.00000 PrimaryPropertyType_Laboratory 0.10908 PrimaryPropertyType_Large Office 0.58188 PrimaryPropertyType_Low-Rise Multifamily -0.27099 PrimaryPropertyType_Medical Office 0.24067 PrimaryPropertyType_Mixed Use Property -0.03516 PrimaryPropertyType_Non-Refrigerated Warehouse -0.65810 PrimaryPropertyType_Office -0.00000 PrimaryPropertyType_Other -0.25524 PrimaryPropertyType_Refrigerated Warehouse -0.18283 PrimaryPropertyType_Residence Hall -0.00000 PrimaryPropertyType_Residence Hall/Dormitory -0.83456 PrimaryPropertyType_Restaurant -0.00000 PrimaryPropertyType_Retail Store 0.24894 PrimaryPropertyType_Self-Storage Facility -2.09233 PrimaryPropertyType_Senior Care Community 0.49603 PrimaryPropertyType_Small- and Mid-Sized Office 0.00000 PrimaryPropertyType_Supermarket / Grocery Store 1.49682 PrimaryPropertyType_Supermarket/Grocery Store 1.41757 PrimaryPropertyType_Warehouse -0.50920 PrimaryPropertyType_Worship Facility -1.82458 LargestPropertyUseType_Automobile Dealership -0.33107 LargestPropertyUseType_Bank Branch -0.24536 LargestPropertyUseType_College/University 0.31065 LargestPropertyUseType_Distribution Center -1.24127 LargestPropertyUseType_Hotel 0.16140 LargestPropertyUseType_K-12 School -0.00000 LargestPropertyUseType_Laboratory 2.09745 LargestPropertyUseType_Library -0.28361 LargestPropertyUseType_Manufacturing/Industrial Plant 0.29174 LargestPropertyUseType_Medical Office 0.40579 LargestPropertyUseType_Multifamily Housing -0.91447 LargestPropertyUseType_Museum 0.69862 LargestPropertyUseType_Non-Refrigerated Warehouse -0.19411 LargestPropertyUseType_Office 0.31506 LargestPropertyUseType_Other -0.38232 LargestPropertyUseType_Other - Education -0.19269 LargestPropertyUseType_Other - Entertainment/Public Assembly -0.05940 LargestPropertyUseType_Other - Lodging/Residential -0.94571 LargestPropertyUseType_Other - Mall -0.07385 LargestPropertyUseType_Other - Recreation 0.37399 LargestPropertyUseType_Other - Services -1.76783 LargestPropertyUseType_Other/Specialty Hospital 1.02349 LargestPropertyUseType_Parking 0.26899 LargestPropertyUseType_Refrigerated Warehouse -0.00000 LargestPropertyUseType_Repair Services (Vehicle, Shoe, Locksmith, etc) -1.60620 LargestPropertyUseType_Residence Hall/Dormitory -0.00000 LargestPropertyUseType_Restaurant 0.56612 LargestPropertyUseType_Retail Store -0.72849 LargestPropertyUseType_Self-Storage Facility -0.00000 LargestPropertyUseType_Senior Care Community 0.00000 LargestPropertyUseType_Social/Meeting Hall -0.16628 LargestPropertyUseType_Strip Mall 0.13393 LargestPropertyUseType_Supermarket/Grocery Store -1.82480 LargestPropertyUseType_Urgent Care/Clinic/Other Outpatient 2.17957 LargestPropertyUseType_Worship Facility -0.00000 SecondLargestPropertyUseType_Aucun ou Non Renseigné -0.38524 SecondLargestPropertyUseType_Bank Branch -1.11719 SecondLargestPropertyUseType_Bar/Nightclub -3.63047 SecondLargestPropertyUseType_Data Center 0.38125 SecondLargestPropertyUseType_Distribution Center -0.17866 SecondLargestPropertyUseType_Fitness Center/Health Club/Gym -0.20504 SecondLargestPropertyUseType_K-12 School 0.37470 SecondLargestPropertyUseType_Laboratory 1.08710 SecondLargestPropertyUseType_Manufacturing/Industrial Plant 0.60006 SecondLargestPropertyUseType_Medical Office 0.17037 SecondLargestPropertyUseType_Movie Theater 0.00000 SecondLargestPropertyUseType_Multifamily Housing -0.00000 SecondLargestPropertyUseType_Non-Refrigerated Warehouse -0.00000 SecondLargestPropertyUseType_Office 0.63526 SecondLargestPropertyUseType_Other -0.41423 SecondLargestPropertyUseType_Other - Entertainment/Public Assembly -0.14646 SecondLargestPropertyUseType_Other - Services 0.10352 SecondLargestPropertyUseType_Parking -0.14492 SecondLargestPropertyUseType_Repair Services (Vehicle, Shoe, Locksmith, etc) 0.00000 SecondLargestPropertyUseType_Restaurant 0.38846 SecondLargestPropertyUseType_Retail Store -0.56639 SecondLargestPropertyUseType_Self-Storage Facility -0.00000 SecondLargestPropertyUseType_Supermarket/Grocery Store -1.02877 ThirdLargestPropertyUseType_Aucun ou Non Renseigné -0.23471 ThirdLargestPropertyUseType_Bank Branch -0.00408 ThirdLargestPropertyUseType_Data Center -0.53480 ThirdLargestPropertyUseType_Financial Office -0.18320 ThirdLargestPropertyUseType_Fitness Center/Health Club/Gym -0.12502 ThirdLargestPropertyUseType_Food Service -0.59489 ThirdLargestPropertyUseType_Laboratory 1.93993 ThirdLargestPropertyUseType_Medical Office -0.48726 ThirdLargestPropertyUseType_Multifamily Housing -0.30949 ThirdLargestPropertyUseType_Non-Refrigerated Warehouse 0.32846 ThirdLargestPropertyUseType_Office 0.83028 ThirdLargestPropertyUseType_Other 0.00000 ThirdLargestPropertyUseType_Other - Entertainment/Public Assembly -0.00000 ThirdLargestPropertyUseType_Other - Recreation 0.38770 ThirdLargestPropertyUseType_Parking 0.00000 ThirdLargestPropertyUseType_Pre-school/Daycare -0.49740 ThirdLargestPropertyUseType_Restaurant -0.05337 ThirdLargestPropertyUseType_Retail Store -0.55152 ThirdLargestPropertyUseType_Social/Meeting Hall -0.09999 ThirdLargestPropertyUseType_Swimming Pool 0.05146 ZipCode_98101 0.18790 ZipCode_98102 -0.26170 ZipCode_98103 0.06094 ZipCode_98104 -0.07141 ZipCode_98105 -0.09022 ZipCode_98106 0.08208 ZipCode_98107 -0.00000 ZipCode_98108 -0.28151 ZipCode_98109 -0.00000 ZipCode_98112 0.00000 ZipCode_98115 -0.00000 ZipCode_98116 0.57790 ZipCode_98117 0.31322 ZipCode_98118 -0.43252 ZipCode_98119 -0.26119 ZipCode_98121 0.00000 ZipCode_98122 -0.20105 ZipCode_98125 0.31950 ZipCode_98133 0.16918 ZipCode_98134 -0.00000 ZipCode_98144 -0.17081 ZipCode_98199 -0.35739 dtype: float64
Observons maintenant la pénalisation des variables en fonction de alpha :
plt.figure(figsize=(15,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.xlabel('alpha')
plt.ylabel('coeflc')
plt.axis('tight')
plt.show()
**154 variables ont été retenues par notre modèle** et 29 ont donc été éliminées.
print('Nombre de variables sélectionnées:',s[s != 0].shape[0])
print('Nombre de variables éliminées:',s[s == 0].shape[0])
Nombre de variables sélectionnées: 154 Nombre de variables éliminées: 29
Liste des variables sélectionnées :
s[s!=0].index.tolist()
['NumberofFloors', 'PropertyGFATotal', 'PropertyGFABuilding', 'LargestPropertyUseTypeGFA', 'DefaultData', 'Outlier', 'Latitude', 'Longitude', 'ageBuilding', 'Proportion_NaturalGas', 'Proportion_Steam', 'YearsENERGYSTARCertified_2008', 'YearsENERGYSTARCertified_2012', 'YearsENERGYSTARCertified_2013', 'YearsENERGYSTARCertified_2014', 'YearsENERGYSTARCertified_2015', 'YearsENERGYSTARCertified_2016', 'ListOfAllPropertyUseTypes_Data Center', 'ListOfAllPropertyUseTypes_Hotel', 'ListOfAllPropertyUseTypes_K-12 School', 'ListOfAllPropertyUseTypes_Multifamily Housing', 'ListOfAllPropertyUseTypes_Non-Refrigerated Warehouse', 'ListOfAllPropertyUseTypes_Office', 'ListOfAllPropertyUseTypes_Other', 'ListOfAllPropertyUseTypes_Parking', 'ListOfAllPropertyUseTypes_Restaurant', 'ListOfAllPropertyUseTypes_Retail Store', 'ListOfAllPropertyUseTypes_Supermarket/Grocery Store', 'ListOfAllPropertyUseTypes_Worship Facility', 'Neighborhood_CENTRAL', 'Neighborhood_DELRIDGE', 'Neighborhood_DOWNTOWN', 'Neighborhood_EAST', 'Neighborhood_GREATER DUWAMISH', 'Neighborhood_LAKE UNION', 'Neighborhood_MAGNOLIA / QUEEN ANNE', 'Neighborhood_NORTH', 'Neighborhood_NORTHEAST', 'Neighborhood_NORTHWEST', 'Neighborhood_SOUTHEAST', 'Neighborhood_SOUTHWEST', 'CouncilDistrictCode_1', 'CouncilDistrictCode_2', 'CouncilDistrictCode_3', 'CouncilDistrictCode_4', 'CouncilDistrictCode_5', 'CouncilDistrictCode_6', 'CouncilDistrictCode_7', 'BuildingType_Campus', 'BuildingType_NonResidential', 'BuildingType_Nonresidential COS', 'BuildingType_SPS-District K-12', 'ComplianceStatus_Error - Correct Default Data', 'PrimaryPropertyType_College/University', 'PrimaryPropertyType_Laboratory', 'PrimaryPropertyType_Large Office', 'PrimaryPropertyType_Low-Rise Multifamily', 'PrimaryPropertyType_Medical Office', 'PrimaryPropertyType_Mixed Use Property', 'PrimaryPropertyType_Non-Refrigerated Warehouse', 'PrimaryPropertyType_Other', 'PrimaryPropertyType_Refrigerated Warehouse', 'PrimaryPropertyType_Residence Hall/Dormitory', 'PrimaryPropertyType_Retail Store', 'PrimaryPropertyType_Self-Storage Facility', 'PrimaryPropertyType_Senior Care Community', 'PrimaryPropertyType_Supermarket / Grocery Store', 'PrimaryPropertyType_Supermarket/Grocery Store', 'PrimaryPropertyType_Warehouse', 'PrimaryPropertyType_Worship Facility', 'LargestPropertyUseType_Automobile Dealership', 'LargestPropertyUseType_Bank Branch', 'LargestPropertyUseType_College/University', 'LargestPropertyUseType_Distribution Center', 'LargestPropertyUseType_Hotel', 'LargestPropertyUseType_Laboratory', 'LargestPropertyUseType_Library', 'LargestPropertyUseType_Manufacturing/Industrial Plant', 'LargestPropertyUseType_Medical Office', 'LargestPropertyUseType_Multifamily Housing', 'LargestPropertyUseType_Museum', 'LargestPropertyUseType_Non-Refrigerated Warehouse', 'LargestPropertyUseType_Office', 'LargestPropertyUseType_Other', 'LargestPropertyUseType_Other - Education', 'LargestPropertyUseType_Other - Entertainment/Public Assembly', 'LargestPropertyUseType_Other - Lodging/Residential', 'LargestPropertyUseType_Other - Mall', 'LargestPropertyUseType_Other - Recreation', 'LargestPropertyUseType_Other - Services', 'LargestPropertyUseType_Other/Specialty Hospital', 'LargestPropertyUseType_Parking', 'LargestPropertyUseType_Refrigerated Warehouse', 'LargestPropertyUseType_Repair Services (Vehicle, Shoe, Locksmith, etc)', 'LargestPropertyUseType_Restaurant', 'LargestPropertyUseType_Retail Store', 'LargestPropertyUseType_Self-Storage Facility', 'LargestPropertyUseType_Senior Care Community', 'LargestPropertyUseType_Social/Meeting Hall', 'LargestPropertyUseType_Strip Mall', 'LargestPropertyUseType_Supermarket/Grocery Store', 'LargestPropertyUseType_Urgent Care/Clinic/Other Outpatient', 'LargestPropertyUseType_Worship Facility', 'SecondLargestPropertyUseType_Aucun ou Non Renseigné', 'SecondLargestPropertyUseType_Bank Branch', 'SecondLargestPropertyUseType_Bar/Nightclub', 'SecondLargestPropertyUseType_Data Center', 'SecondLargestPropertyUseType_Distribution Center', 'SecondLargestPropertyUseType_Fitness Center/Health Club/Gym', 'SecondLargestPropertyUseType_K-12 School', 'SecondLargestPropertyUseType_Laboratory', 'SecondLargestPropertyUseType_Manufacturing/Industrial Plant', 'SecondLargestPropertyUseType_Medical Office', 'SecondLargestPropertyUseType_Office', 'SecondLargestPropertyUseType_Other', 'SecondLargestPropertyUseType_Other - Entertainment/Public Assembly', 'SecondLargestPropertyUseType_Other - Services', 'SecondLargestPropertyUseType_Parking', 'SecondLargestPropertyUseType_Restaurant', 'SecondLargestPropertyUseType_Retail Store', 'SecondLargestPropertyUseType_Supermarket/Grocery Store', 'ThirdLargestPropertyUseType_Aucun ou Non Renseigné', 'ThirdLargestPropertyUseType_Bank Branch', 'ThirdLargestPropertyUseType_Data Center', 'ThirdLargestPropertyUseType_Financial Office', 'ThirdLargestPropertyUseType_Fitness Center/Health Club/Gym', 'ThirdLargestPropertyUseType_Food Service', 'ThirdLargestPropertyUseType_Laboratory', 'ThirdLargestPropertyUseType_Medical Office', 'ThirdLargestPropertyUseType_Multifamily Housing', 'ThirdLargestPropertyUseType_Non-Refrigerated Warehouse', 'ThirdLargestPropertyUseType_Office', 'ThirdLargestPropertyUseType_Other - Recreation', 'ThirdLargestPropertyUseType_Pre-school/Daycare', 'ThirdLargestPropertyUseType_Restaurant', 'ThirdLargestPropertyUseType_Retail Store', 'ThirdLargestPropertyUseType_Social/Meeting Hall', 'ThirdLargestPropertyUseType_Swimming Pool', 'ZipCode_98101', 'ZipCode_98102', 'ZipCode_98103', 'ZipCode_98104', 'ZipCode_98105', 'ZipCode_98106', 'ZipCode_98108', 'ZipCode_98116', 'ZipCode_98117', 'ZipCode_98118', 'ZipCode_98119', 'ZipCode_98122', 'ZipCode_98125', 'ZipCode_98133', 'ZipCode_98144', 'ZipCode_98199']
Liste des variables éliminées :
s[s==0].index.tolist()
['Proportion_Electricity', 'YearsENERGYSTARCertified_2009', 'ListOfAllPropertyUseTypes_Distribution Center', 'ListOfAllPropertyUseTypes_Medical Office', 'Neighborhood_BALLARD', 'ComplianceStatus_Compliant', 'PrimaryPropertyType_Distribution Center', 'PrimaryPropertyType_Hotel', 'PrimaryPropertyType_K-12 School', 'PrimaryPropertyType_Office', 'PrimaryPropertyType_Residence Hall', 'PrimaryPropertyType_Restaurant', 'PrimaryPropertyType_Small- and Mid-Sized Office', 'LargestPropertyUseType_K-12 School', 'LargestPropertyUseType_Residence Hall/Dormitory', 'SecondLargestPropertyUseType_Movie Theater', 'SecondLargestPropertyUseType_Multifamily Housing', 'SecondLargestPropertyUseType_Non-Refrigerated Warehouse', 'SecondLargestPropertyUseType_Repair Services (Vehicle, Shoe, Locksmith, etc)', 'SecondLargestPropertyUseType_Self-Storage Facility', 'ThirdLargestPropertyUseType_Other', 'ThirdLargestPropertyUseType_Other - Entertainment/Public Assembly', 'ThirdLargestPropertyUseType_Parking', 'ZipCode_98107', 'ZipCode_98109', 'ZipCode_98112', 'ZipCode_98115', 'ZipCode_98121', 'ZipCode_98134']
Je filtre 'df' avec les colonnes sélectionnées par LassoCV
et j'enregistre le résultat dans un nouveau DataFrame 'df_Total'.
Je rajoute également à ce DataFrame la colonne 'ENERGYSTARScore' ainsi
que la colonne target qui lui correspond.
df_Total = df[X.columns[s != 0].tolist()+['ENERGYSTARScore','TotalGHGEmissions']]
print('Dimensions du DataFrame \'df_Total\':',df_Total.shape)
Dimensions du DataFrame 'df_Total': (1425, 156)
Je répète maintenant exactement le même protocole
de sélection de variable que précédemment :
Création de nos variables X (features) et y (target) :
X = df.drop(['TotalGHGEmissions','SiteEnergyUse','ENERGYSTARScore'], axis=1)
y = df.SiteEnergyUse
Création de nos jeux de train et de test,
avec un random_state défini pour la reproductivité des résultats :
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=RS) # Pour la reproductibilité des résultats
Nous allons maintenant itérer notre modèle avec
plusieurs valeurs possibles d'alpha.
Je définis une gamme assez large d'alphas allant
de 10e-4 à 10e1 découpés en 400 échantillons.
A chaque itération, nous allons enregistrer dans une List :
alphas = np.logspace(-4, 1, 300)
coefs=[]
errors = []
for alpha in alphas:
lcv = LassoCV(alphas=[alpha],
fit_intercept=False,
normalize=False,
cv=5,
max_iter=10000,
random_state=RS)
lcv.fit(X_train, y_train)
coefs.append(lcv.coef_)
errors.append(mean_squared_error(y_test,lcv.predict(X_test)))
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 384.24340787815595, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 375.2853860673561, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400.92816928376004, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 384.615458110118, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.4734537538907, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 508.42725421280676, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 385.3729808568274, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 376.3561827829688, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.006013267409, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 385.8876774622633, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.5950532157619, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 509.8762635012239, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 386.50452786015273, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 377.4291707963217, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.08606609014413, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 387.1500169689096, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.71588636136585, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 511.3291811437941, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 387.6363461950728, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 378.50069605110394, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 404.1671285488344, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388.3768016405406, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408.84060602296387, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 512.7827148910376, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388.76889528090044, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 379.5694080068593, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.24445120570914, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.63615219136193, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409.96369191036086, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 514.2330819346397, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.89862451108274, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 380.6329123587032, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.3127978592571, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 390.95184237200164, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411.0810547250242, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 515.6612986125366, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 391.02199480178643, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 381.6476226133102, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.36290111946806, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.2861106186872, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412.18823440615, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 517.0916518225192, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.13108372343396, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 382.67360777068103, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408.36062091151126, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.5936473517775, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.2981475744965, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 518.5032352607853, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.2197856137171, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 383.67886106546, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409.362359971868, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394.8719450869512, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.37027346899623, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 519.8633370279605, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394.2287795192677, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 384.66265216389945, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410.3338005928627, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.1266634927956, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 415.409014017651, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 521.1164203202686, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 395.24656621168043, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 385.6064302846036, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 411.2827450839874, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 397.35752771760986, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.41953704999696, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 522.3624036121911, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.2434298542734, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 386.5167070899252, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412.19006469548447, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398.57271851125745, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 417.3807469399701, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 523.512002320559, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 397.2074059261767, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 387.39656345162706, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.05799562156255, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.6862649407847, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 418.28857497981403, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 524.6315860562146, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398.1720951034418, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388.2376774066121, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.8584780858727, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400.8191811848429, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 419.1468264430736, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 525.741774812507, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.008280590104, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.02158046114516, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.5702797834509, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.8901482314965, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 419.9368967051033, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 526.7675065049165, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.7460082684881, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.6879264809925, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 415.27077247627267, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.8938162613078, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 420.6495162951966, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 527.7089202051935, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400.36589338815105, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 390.26627043402357, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 415.8119203006565, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.80236806736787, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 421.27769743156125, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 528.578576540514, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400.9793252742447, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 390.7292076711437, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.23435764227037, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 404.6134954718475, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 421.79349021902766, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 529.2748050410253, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.4962312228579, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 390.9703146515057, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.5071283947454, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.3393483495817, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422.18883400949755, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 529.8139989063669, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.899237675845, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 391.14976215950605, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.68346591648054, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.92573553484897, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422.44202560544744, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 530.1802032071448, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.1714616732002, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 391.17927082738174, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.7010262728924, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.39417510903144, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422.48669701022817, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 530.3516628031916, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.2958558645117, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 391.0317892229141, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.5396243547328, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.70619069034, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422.4389325348786, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 530.2613124195781, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.2537565467988, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 390.55041167687983, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.16860575946964, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.8398321710105, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 422.1240673105806, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 529.9908445368362, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.0210649326346, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.95119994064896, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 415.56763346836027, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.7695239725517, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 421.5224964877361, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 529.4236054574677, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.5194790062873, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.11073098291246, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.72165356642614, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.4962005341479, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 420.7701565800818, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 528.5405182594382, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400.85402294599743, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 387.98721112479313, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.5831276462852, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.9436821341474, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 419.7384617676038, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 527.3792651953419, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.9406938375066, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 386.51484517029195, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412.1306689179554, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.1017295514032, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 418.39454613440364, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 525.9292523864694, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398.7530152378649, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 384.4956236343598, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410.3312593013031, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.9359845891623, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.74802942011445, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 523.9508366943002, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 397.25662924480764, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 382.3120601857285, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408.1420371002457, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.42228125728644, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.75567100197435, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 520.8602845739432, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 395.5480865959546, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 379.1357188606451, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405.5520524333063, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 400.51479188788545, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 412.27523508635187, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 517.7146616100074, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.28915740971786, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 378.8994092596942, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 404.1453557064864, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398.1632727973957, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410.4207115858709, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 514.0417300178752, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.57035061340406, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 378.13642893015356, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.5484133612291, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 395.2914770119544, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 409.7315509495788, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 511.83210730007715, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.74297442449665, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 377.10892887202857, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402.64799410815345, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.76117252847365, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408.87381882243284, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 510.6053739806963, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.76918830355964, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 375.775848728496, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.3269501343519, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.0506959104023, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.68428042439604, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 508.96672522696974, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.6383773161988, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 374.1393079202266, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.20657556742736, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.26394727590747, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406.58665394446626, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 506.8489472333091, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.3325600996122, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 372.2530156156018, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 396.7459132308869, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 390.927103021697, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 404.7247080268271, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 503.9403287718061, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 395.8486098212102, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 369.70995306438954, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 393.8280927944132, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.2302676369226, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 401.38201509497753, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 500.74625580682317, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 395.12578848611827, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 365.62614565326453, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 390.6584487482898, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 387.18791870493146, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 398.40928188764303, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 497.13886569039056, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394.1525275121049, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 361.25917716828656, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 386.6099981244563, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 384.5484212561708, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 394.67789042526584, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 492.5454613055779, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.90056457164303, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 356.77046237328454, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 381.8972055730615, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 381.2837546193143, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 390.18622990076943, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 487.01420939587797, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 391.30778689822876, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 351.0327933246929, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 376.2048946775209, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 377.3421068352337, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 385.03440001283786, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 480.4793119886374, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.33209696861303, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 344.4491730589048, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 369.31797625070743, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 372.7289264948437, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 378.91935181869815, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 472.80826036752575, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 387.0082109630985, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336.9170099598252, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 360.96149463910643, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 367.21640386421666, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 371.6538759814426, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 463.6605750272011, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 384.3021386799909, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 327.46529070721107, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 350.7137343259727, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 360.491135982223, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 362.51988918868227, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 452.7842582865011, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 380.9669856479342, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 315.93300384240945, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 339.15477506502003, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 352.2533046773578, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 352.4506890361208, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 439.74610178438184, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 377.071990301651, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 305.12926373040057, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 325.5297174531484, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 342.1776878458037, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 340.2060357995578, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 425.53069739442543, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 374.91329164043117, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 306.7470813256498, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 324.0208120567435, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 330.4642862282639, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 325.43955197797715, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410.6815019833923, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 372.5930660528425, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 309.3856102372404, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 327.4913779212734, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 318.9482891810683, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 327.1075735952927, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407.47521369925244, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 369.8262926049708, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 311.6858589621339, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 330.1763199357148, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.6166051765795, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 329.55692240120396, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 410.35788518430587, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 366.59907692577923, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 314.0953628494494, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.1596879543088, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.61565815080655, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.49234688241273, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414.86477165359565, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 362.97565998738673, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 316.90406212241646, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 335.9610455526943, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 327.332843074127, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336.4262740741416, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 418.9979555413067, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 358.77324861917276, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 319.36985166361535, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 338.5911486729155, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 330.49268446733595, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 340.09959082985944, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 423.0137072023801, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 353.8241765346514, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.5826458805838, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 341.0788552020894, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.616843738378, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 342.38767292896966, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 426.5522835195303, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 348.0431486517657, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.71075288990255, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 342.7124510141859, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336.56453501000317, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 344.6069427634133, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 429.8181682083217, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 341.4983442126477, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 325.60849178644384, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 344.5324289365373, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 339.26785325279315, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 346.4992396813351, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 432.71888676305196, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.5715807772535, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 327.0996783952198, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 346.29230634439887, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 341.7106781385959, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 347.24630516506153, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 435.3163117604734, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 324.85648951628434, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 328.0061974619404, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 347.8399576333573, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 343.9362629946864, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 348.7159311373729, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 437.5510177057483, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 314.66781756331505, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 328.67049760523423, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 349.0969900113537, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 345.83930583914633, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 349.99513905027925, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 439.44422676401973, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 302.5179779818745, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 329.3222840422495, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 350.0358976548393, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 347.3918076003551, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 351.0216076328061, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 440.9792378358432, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 289.17723282304803, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 329.8123871722508, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 350.58200430615415, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 348.5782924983369, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 351.6710087235923, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 442.06486911066577, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.35715458024214, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 329.93620816410464, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 350.76558141780987, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 349.0775525825935, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 351.93800575654683, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 442.673086943333, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259.40569728247556, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 297.5159828740931, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 350.5410671616274, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 349.4629411288828, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 352.02853362569545, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 442.66523790750256, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 241.72150510472983, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 295.2476336718903, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 330.30053740966014, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 349.4250587569444, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 318.5693221268793, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 442.16985645325343, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 221.00814551000974, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 292.2932153937912, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 311.12378400305045, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 349.27107549980184, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 312.2183535737745, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 413.33820339127186, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 223.32638029824483, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 289.0802976094935, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 307.84433136320274, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.7560800293368, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 308.7514693947704, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 399.3226013484557, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 227.9029856822973, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 285.55026550262124, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 304.41929189365163, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 314.60478199950046, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 305.0925915039573, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 392.8588699788944, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 232.3637295987579, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 281.75730925464245, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 300.6496493167887, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 310.4175201604193, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 301.4540044432037, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 375.10039474453947, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 236.8842514497324, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.8281898429047, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 296.5539038498245, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 306.96029593069693, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 297.7415200369047, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 370.556497342174, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 241.13616832085404, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273.4632824846342, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 292.4354363100852, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 303.08186394905863, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 293.2314019484974, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 364.9446340082713, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 245.1217932809124, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 269.1804573769207, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 287.98596153147344, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 296.9426918464281, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 288.50503010897364, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 359.08166345662664, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 248.92381845128938, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.40906947422593, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 282.79641570517947, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 287.2066898620285, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 282.74473144567514, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 353.06396910684225, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252.405880275134, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259.66465761171986, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.8006390206825, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 282.91683663619585, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.5501991135681, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 346.8914661501896, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255.79543240042187, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 254.68592892650634, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 272.68174704917567, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.45796412834665, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 272.0612328132643, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 340.3554690450066, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259.0754060475249, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249.49419005380946, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 267.21366867665927, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273.994239619686, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 266.6345251682185, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.54255860075756, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.17928986384646, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 245.64711171673486, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.04783329721016, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 269.05598338748575, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.9310327825702, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 326.56409026301674, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.938909425357, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 250.11421625985327, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 256.03390502766734, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.53386017124626, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255.00853544306563, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 318.85239597035945, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 267.406046700603, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 254.29642666363588, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 247.62508945008722, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258.0596792916175, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 248.7345458602942, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 311.0942050771002, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 269.53515870334587, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 225.5618952229193, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 241.3876281420754, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252.14762191994652, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 242.25572220858493, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 303.05039208809535, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 271.3903028766025, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 219.28020430587975, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 234.7772015776668, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246.03564779010208, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 235.4921883745103, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 294.82561430530404, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 210.39535820315132, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 212.3930487289723, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 227.90219696415386, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 239.35070834092835, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 228.49127244963142, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 286.27421154808917, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 204.3624760794338, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 205.50808005310284, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220.72491275641056, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 232.85807095049267, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 221.2515025372436, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.4344403963401, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198.25744347058992, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198.71459784559363, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 213.2630673231534, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 226.01608745680443, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 213.75838807508268, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 268.6627009528505, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 192.0659649531708, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191.5869244489091, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 205.70473833975717, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 218.79679165378428, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 205.94827682368737, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258.8895528082755, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 185.18717518939076, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 184.59670789327959, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198.78007037739906, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211.0522322135875, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 197.71441738014232, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 248.9372762974141, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 177.30280739232705, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 176.96365812552557, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190.73495586145077, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 203.1342145531392, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 189.66774946244334, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 238.3888403551686, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169.32279947994323, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168.97890132965756, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 182.20321367690042, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194.79786062900894, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180.78580369621693, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 227.8516804854595, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 161.34938509829715, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 160.87975582460626, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 173.8340534561454, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 186.57884911130168, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171.99051359802604, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 217.28532384625692, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 152.95377014790427, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 153.27555924643048, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165.31087700229477, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 178.31879752994143, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 162.7763605613659, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 206.9727549867177, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144.0311379572181, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144.22318835167266, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 156.58842105426424, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169.8755047219522, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 153.7489208896286, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 196.00875955585195, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 135.1648545365773, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 135.74571594784425, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 147.6861974457629, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 161.2306465958934, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144.55574536869324, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 184.8060919875138, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126.09848804094077, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 127.11996697423206, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 138.93848733449454, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 152.35252278453788, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 135.16365430826488, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 173.4509931841818, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 116.93833564859233, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 118.12205388242512, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 130.06539919967213, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 143.27019712665492, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 125.7445647341674, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 161.86883580215476, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 107.47329751210509, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 109.33270255252751, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121.05076706935336, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 133.86096442164694, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 116.21037501613455, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 150.29563145156408, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98.54031971229949, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100.55348820416714, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 111.95500446745802, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124.43669021591813, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 106.5930558732872, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 138.49777861013047, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 89.5698882418003, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 91.6518099011771, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 102.7142761151731, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 114.85460993935135, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 96.92937010753872, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126.75848967059255, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 80.56709733625598, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 82.43154726213788, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 93.47696750262082, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 104.57013329905249, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 87.2676644648102, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 114.97823364848273, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 71.61131729084661, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 73.4087360268311, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.79839276341727, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 95.2604160070722, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 77.66885177560425, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 103.95319797246589, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62.75897617697535, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64.41680287519398, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 74.46359765556474, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 84.7031431350357, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 68.33955043328046, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 92.6446814751107, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54.15997276217445, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.68409892830982, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65.2258476091896, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 75.36408170312257, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59.141183310608426, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81.43388643835954, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46.080734290871305, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47.205137745860156, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.32174938754747, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 66.05157768910226, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50.098684010579746, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 70.32519978414712, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37.93822672612828, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39.04063977957958, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47.62961149065177, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.10734087303115, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 41.366203059845475, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59.602148381838674, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30.025009280430368, tolerance: 28.3163253491455 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30.444867403665057, tolerance: 28.412371546730988 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39.008986777080736, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48.35080045779216, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 32.86562228308753, tolerance: 28.34968010185227 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49.161217700044745, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30.99096684887104, tolerance: 28.211881665091333 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39.82455355600132, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39.29864869194762, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:525: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 31.673003333113797, tolerance: 28.267140482981592 model = cd_fast.enet_coordinate_descent_gram(
Graphique illustrant l'erreur quadratique moyenne en fonction de alpha :
Alpha semble atteindre un minimum entre 10e-3 et 10e-2
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, errors)
ax.set_xscale('log')
plt.xlabel('alpha')
plt.ylabel('erreur: MSE')
plt.axis('tight')
plt.show()
Affichons précisément la valeur de alpha qui minimise l'erreur MSE (Mean Square Error) :
df_alpha = pd.DataFrame({'alpha':alphas,'MSE':errors})
df_alpha[df_alpha.index == df_alpha.MSE.argmin()]
| alpha | MSE | |
|---|---|---|
| 48 | 0.00063 | 1.01825 |
Observons maintenant, pour le meilleur alpha calculé,
les coefficients affectés à chacune de nos variables.
Un coefficient à 0 signifie que le modèle a pénalisé
la variable correspondante au point de l'éliminer.
s = pd.Series(coefs[df_alpha.MSE.argmin()], index=X.columns)
s
NumberofFloors 0.22910 PropertyGFATotal 0.14055 PropertyGFABuilding 0.04146 LargestPropertyUseTypeGFA 9.82820 DefaultData -0.61101 Outlier 0.00810 Latitude -0.29653 Longitude 0.01180 ageBuilding -0.19613 Proportion_Electricity 0.00000 Proportion_NaturalGas 0.30640 Proportion_Steam 0.11713 YearsENERGYSTARCertified_2008 0.00000 YearsENERGYSTARCertified_2009 0.00000 YearsENERGYSTARCertified_2012 0.09791 YearsENERGYSTARCertified_2013 0.15437 YearsENERGYSTARCertified_2014 -0.00000 YearsENERGYSTARCertified_2015 0.07025 YearsENERGYSTARCertified_2016 -0.09800 ListOfAllPropertyUseTypes_Data Center 0.85944 ListOfAllPropertyUseTypes_Distribution Center 0.00000 ListOfAllPropertyUseTypes_Hotel -0.06629 ListOfAllPropertyUseTypes_K-12 School -0.63418 ListOfAllPropertyUseTypes_Medical Office -0.00000 ListOfAllPropertyUseTypes_Multifamily Housing 0.00000 ListOfAllPropertyUseTypes_Non-Refrigerated Warehouse -0.31308 ListOfAllPropertyUseTypes_Office -0.52463 ListOfAllPropertyUseTypes_Other 0.32917 ListOfAllPropertyUseTypes_Parking -0.11015 ListOfAllPropertyUseTypes_Restaurant 0.00000 ListOfAllPropertyUseTypes_Retail Store 0.22421 ListOfAllPropertyUseTypes_Supermarket/Grocery Store 1.29835 ListOfAllPropertyUseTypes_Worship Facility 0.14166 Neighborhood_BALLARD 0.00000 Neighborhood_CENTRAL -0.49464 Neighborhood_DELRIDGE 0.01350 Neighborhood_DOWNTOWN -0.01770 Neighborhood_EAST -0.17423 Neighborhood_GREATER DUWAMISH -0.01338 Neighborhood_LAKE UNION -0.00000 Neighborhood_MAGNOLIA / QUEEN ANNE 0.00000 Neighborhood_NORTH -0.06813 Neighborhood_NORTHEAST 0.27592 Neighborhood_NORTHWEST -0.00781 Neighborhood_SOUTHEAST -0.00000 Neighborhood_SOUTHWEST -0.16525 CouncilDistrictCode_1 -0.64166 CouncilDistrictCode_2 -0.56550 CouncilDistrictCode_3 -0.00000 CouncilDistrictCode_4 -0.16693 CouncilDistrictCode_5 -0.00000 CouncilDistrictCode_6 -0.00000 CouncilDistrictCode_7 -0.32621 BuildingType_Campus -16.84138 BuildingType_NonResidential -17.62943 BuildingType_Nonresidential COS -17.33748 BuildingType_SPS-District K-12 -16.75405 ComplianceStatus_Compliant -0.10532 ComplianceStatus_Error - Correct Default Data -0.27595 PrimaryPropertyType_College/University -0.27536 PrimaryPropertyType_Distribution Center -0.00000 PrimaryPropertyType_Hotel 0.00000 PrimaryPropertyType_K-12 School -0.20027 PrimaryPropertyType_Laboratory 0.00000 PrimaryPropertyType_Large Office 0.67922 PrimaryPropertyType_Low-Rise Multifamily -0.00000 PrimaryPropertyType_Medical Office 0.56374 PrimaryPropertyType_Mixed Use Property -0.00000 PrimaryPropertyType_Non-Refrigerated Warehouse -0.51975 PrimaryPropertyType_Office -0.01191 PrimaryPropertyType_Other -0.18549 PrimaryPropertyType_Refrigerated Warehouse 0.00000 PrimaryPropertyType_Residence Hall -0.00000 PrimaryPropertyType_Residence Hall/Dormitory -0.63786 PrimaryPropertyType_Restaurant 0.00000 PrimaryPropertyType_Retail Store 0.00000 PrimaryPropertyType_Self-Storage Facility -2.06530 PrimaryPropertyType_Senior Care Community 0.48299 PrimaryPropertyType_Small- and Mid-Sized Office 0.00000 PrimaryPropertyType_Supermarket / Grocery Store 0.81280 PrimaryPropertyType_Supermarket/Grocery Store 0.89920 PrimaryPropertyType_Warehouse -0.30379 PrimaryPropertyType_Worship Facility -1.65444 LargestPropertyUseType_Automobile Dealership -0.13728 LargestPropertyUseType_Bank Branch -0.00000 LargestPropertyUseType_College/University 0.00000 LargestPropertyUseType_Distribution Center -1.16561 LargestPropertyUseType_Hotel 0.00000 LargestPropertyUseType_K-12 School -0.00000 LargestPropertyUseType_Laboratory 1.87757 LargestPropertyUseType_Library -0.12091 LargestPropertyUseType_Manufacturing/Industrial Plant 0.50136 LargestPropertyUseType_Medical Office 0.15818 LargestPropertyUseType_Multifamily Housing -0.74717 LargestPropertyUseType_Museum 0.71004 LargestPropertyUseType_Non-Refrigerated Warehouse -0.25331 LargestPropertyUseType_Office 0.29948 LargestPropertyUseType_Other -0.29496 LargestPropertyUseType_Other - Education -0.00000 LargestPropertyUseType_Other - Entertainment/Public Assembly -0.01882 LargestPropertyUseType_Other - Lodging/Residential -0.58888 LargestPropertyUseType_Other - Mall -0.00000 LargestPropertyUseType_Other - Recreation 0.34646 LargestPropertyUseType_Other - Services -1.59182 LargestPropertyUseType_Other/Specialty Hospital 0.79016 LargestPropertyUseType_Parking 0.32566 LargestPropertyUseType_Refrigerated Warehouse 0.00000 LargestPropertyUseType_Repair Services (Vehicle, Shoe, Locksmith, etc) -1.51455 LargestPropertyUseType_Residence Hall/Dormitory -0.01703 LargestPropertyUseType_Restaurant 0.73143 LargestPropertyUseType_Retail Store -0.29651 LargestPropertyUseType_Self-Storage Facility -0.00000 LargestPropertyUseType_Senior Care Community 0.00000 LargestPropertyUseType_Social/Meeting Hall -0.00000 LargestPropertyUseType_Strip Mall 0.09567 LargestPropertyUseType_Supermarket/Grocery Store -0.61142 LargestPropertyUseType_Urgent Care/Clinic/Other Outpatient 1.70600 LargestPropertyUseType_Worship Facility -0.00000 SecondLargestPropertyUseType_Aucun ou Non Renseigné -0.31615 SecondLargestPropertyUseType_Bank Branch -0.92549 SecondLargestPropertyUseType_Bar/Nightclub -2.81698 SecondLargestPropertyUseType_Data Center 0.52675 SecondLargestPropertyUseType_Distribution Center -0.22887 SecondLargestPropertyUseType_Fitness Center/Health Club/Gym -0.06612 SecondLargestPropertyUseType_K-12 School 0.00000 SecondLargestPropertyUseType_Laboratory 1.01885 SecondLargestPropertyUseType_Manufacturing/Industrial Plant 0.44017 SecondLargestPropertyUseType_Medical Office 0.15005 SecondLargestPropertyUseType_Movie Theater -0.00000 SecondLargestPropertyUseType_Multifamily Housing 0.00000 SecondLargestPropertyUseType_Non-Refrigerated Warehouse -0.08188 SecondLargestPropertyUseType_Office 0.46442 SecondLargestPropertyUseType_Other -0.40720 SecondLargestPropertyUseType_Other - Entertainment/Public Assembly -0.00000 SecondLargestPropertyUseType_Other - Services 0.12867 SecondLargestPropertyUseType_Parking -0.15137 SecondLargestPropertyUseType_Repair Services (Vehicle, Shoe, Locksmith, etc) 0.00000 SecondLargestPropertyUseType_Restaurant 0.37895 SecondLargestPropertyUseType_Retail Store -0.40134 SecondLargestPropertyUseType_Self-Storage Facility -0.00000 SecondLargestPropertyUseType_Supermarket/Grocery Store -0.26976 ThirdLargestPropertyUseType_Aucun ou Non Renseigné -0.19903 ThirdLargestPropertyUseType_Bank Branch -0.00000 ThirdLargestPropertyUseType_Data Center -0.39161 ThirdLargestPropertyUseType_Financial Office -0.00000 ThirdLargestPropertyUseType_Fitness Center/Health Club/Gym -0.00000 ThirdLargestPropertyUseType_Food Service -0.56398 ThirdLargestPropertyUseType_Laboratory 1.47672 ThirdLargestPropertyUseType_Medical Office -0.32024 ThirdLargestPropertyUseType_Multifamily Housing -0.04221 ThirdLargestPropertyUseType_Non-Refrigerated Warehouse 0.17451 ThirdLargestPropertyUseType_Office 0.73069 ThirdLargestPropertyUseType_Other 0.00000 ThirdLargestPropertyUseType_Other - Entertainment/Public Assembly 0.00000 ThirdLargestPropertyUseType_Other - Recreation 0.43145 ThirdLargestPropertyUseType_Parking -0.00000 ThirdLargestPropertyUseType_Pre-school/Daycare -0.47481 ThirdLargestPropertyUseType_Restaurant -0.07587 ThirdLargestPropertyUseType_Retail Store -0.37965 ThirdLargestPropertyUseType_Social/Meeting Hall -0.00000 ThirdLargestPropertyUseType_Swimming Pool 0.18620 ZipCode_98101 0.20612 ZipCode_98102 -0.16777 ZipCode_98103 -0.00000 ZipCode_98104 -0.07794 ZipCode_98105 -0.01363 ZipCode_98106 0.10869 ZipCode_98107 -0.09459 ZipCode_98108 -0.25948 ZipCode_98109 0.19251 ZipCode_98112 0.02528 ZipCode_98115 0.00000 ZipCode_98116 0.54678 ZipCode_98117 0.25815 ZipCode_98118 -0.20889 ZipCode_98119 -0.04669 ZipCode_98121 -0.00000 ZipCode_98122 -0.27597 ZipCode_98125 0.17968 ZipCode_98133 0.22430 ZipCode_98134 -0.00000 ZipCode_98144 -0.11412 ZipCode_98199 -0.25376 dtype: float64
Observons maintenant la pénalisation des variables en fonction de alpha :
plt.figure(figsize=(15,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.xlabel('alpha')
plt.ylabel('coeflc')
plt.axis('tight')
plt.show()
**133 variables ont été retenues par notre modèle** et 50 ont donc été éliminées.
print('Nombre de variables sélectionnées:',s[s != 0].shape[0])
print('Nombre de variables éliminées:',s[s == 0].shape[0])
Nombre de variables sélectionnées: 133 Nombre de variables éliminées: 50
Liste des variables sélectionnées :
s[s!=0].index.tolist()
['NumberofFloors', 'PropertyGFATotal', 'PropertyGFABuilding', 'LargestPropertyUseTypeGFA', 'DefaultData', 'Outlier', 'Latitude', 'Longitude', 'ageBuilding', 'Proportion_NaturalGas', 'Proportion_Steam', 'YearsENERGYSTARCertified_2012', 'YearsENERGYSTARCertified_2013', 'YearsENERGYSTARCertified_2015', 'YearsENERGYSTARCertified_2016', 'ListOfAllPropertyUseTypes_Data Center', 'ListOfAllPropertyUseTypes_Hotel', 'ListOfAllPropertyUseTypes_K-12 School', 'ListOfAllPropertyUseTypes_Non-Refrigerated Warehouse', 'ListOfAllPropertyUseTypes_Office', 'ListOfAllPropertyUseTypes_Other', 'ListOfAllPropertyUseTypes_Parking', 'ListOfAllPropertyUseTypes_Retail Store', 'ListOfAllPropertyUseTypes_Supermarket/Grocery Store', 'ListOfAllPropertyUseTypes_Worship Facility', 'Neighborhood_CENTRAL', 'Neighborhood_DELRIDGE', 'Neighborhood_DOWNTOWN', 'Neighborhood_EAST', 'Neighborhood_GREATER DUWAMISH', 'Neighborhood_NORTH', 'Neighborhood_NORTHEAST', 'Neighborhood_NORTHWEST', 'Neighborhood_SOUTHWEST', 'CouncilDistrictCode_1', 'CouncilDistrictCode_2', 'CouncilDistrictCode_4', 'CouncilDistrictCode_7', 'BuildingType_Campus', 'BuildingType_NonResidential', 'BuildingType_Nonresidential COS', 'BuildingType_SPS-District K-12', 'ComplianceStatus_Compliant', 'ComplianceStatus_Error - Correct Default Data', 'PrimaryPropertyType_College/University', 'PrimaryPropertyType_K-12 School', 'PrimaryPropertyType_Large Office', 'PrimaryPropertyType_Medical Office', 'PrimaryPropertyType_Non-Refrigerated Warehouse', 'PrimaryPropertyType_Office', 'PrimaryPropertyType_Other', 'PrimaryPropertyType_Residence Hall/Dormitory', 'PrimaryPropertyType_Self-Storage Facility', 'PrimaryPropertyType_Senior Care Community', 'PrimaryPropertyType_Supermarket / Grocery Store', 'PrimaryPropertyType_Supermarket/Grocery Store', 'PrimaryPropertyType_Warehouse', 'PrimaryPropertyType_Worship Facility', 'LargestPropertyUseType_Automobile Dealership', 'LargestPropertyUseType_Distribution Center', 'LargestPropertyUseType_K-12 School', 'LargestPropertyUseType_Laboratory', 'LargestPropertyUseType_Library', 'LargestPropertyUseType_Manufacturing/Industrial Plant', 'LargestPropertyUseType_Medical Office', 'LargestPropertyUseType_Multifamily Housing', 'LargestPropertyUseType_Museum', 'LargestPropertyUseType_Non-Refrigerated Warehouse', 'LargestPropertyUseType_Office', 'LargestPropertyUseType_Other', 'LargestPropertyUseType_Other - Entertainment/Public Assembly', 'LargestPropertyUseType_Other - Lodging/Residential', 'LargestPropertyUseType_Other - Recreation', 'LargestPropertyUseType_Other - Services', 'LargestPropertyUseType_Other/Specialty Hospital', 'LargestPropertyUseType_Parking', 'LargestPropertyUseType_Repair Services (Vehicle, Shoe, Locksmith, etc)', 'LargestPropertyUseType_Residence Hall/Dormitory', 'LargestPropertyUseType_Restaurant', 'LargestPropertyUseType_Retail Store', 'LargestPropertyUseType_Self-Storage Facility', 'LargestPropertyUseType_Senior Care Community', 'LargestPropertyUseType_Strip Mall', 'LargestPropertyUseType_Supermarket/Grocery Store', 'LargestPropertyUseType_Urgent Care/Clinic/Other Outpatient', 'SecondLargestPropertyUseType_Aucun ou Non Renseigné', 'SecondLargestPropertyUseType_Bank Branch', 'SecondLargestPropertyUseType_Bar/Nightclub', 'SecondLargestPropertyUseType_Data Center', 'SecondLargestPropertyUseType_Distribution Center', 'SecondLargestPropertyUseType_Fitness Center/Health Club/Gym', 'SecondLargestPropertyUseType_Laboratory', 'SecondLargestPropertyUseType_Manufacturing/Industrial Plant', 'SecondLargestPropertyUseType_Medical Office', 'SecondLargestPropertyUseType_Non-Refrigerated Warehouse', 'SecondLargestPropertyUseType_Office', 'SecondLargestPropertyUseType_Other', 'SecondLargestPropertyUseType_Other - Services', 'SecondLargestPropertyUseType_Parking', 'SecondLargestPropertyUseType_Restaurant', 'SecondLargestPropertyUseType_Retail Store', 'SecondLargestPropertyUseType_Supermarket/Grocery Store', 'ThirdLargestPropertyUseType_Aucun ou Non Renseigné', 'ThirdLargestPropertyUseType_Data Center', 'ThirdLargestPropertyUseType_Food Service', 'ThirdLargestPropertyUseType_Laboratory', 'ThirdLargestPropertyUseType_Medical Office', 'ThirdLargestPropertyUseType_Multifamily Housing', 'ThirdLargestPropertyUseType_Non-Refrigerated Warehouse', 'ThirdLargestPropertyUseType_Office', 'ThirdLargestPropertyUseType_Other - Recreation', 'ThirdLargestPropertyUseType_Pre-school/Daycare', 'ThirdLargestPropertyUseType_Restaurant', 'ThirdLargestPropertyUseType_Retail Store', 'ThirdLargestPropertyUseType_Swimming Pool', 'ZipCode_98101', 'ZipCode_98102', 'ZipCode_98104', 'ZipCode_98105', 'ZipCode_98106', 'ZipCode_98107', 'ZipCode_98108', 'ZipCode_98109', 'ZipCode_98112', 'ZipCode_98116', 'ZipCode_98117', 'ZipCode_98118', 'ZipCode_98119', 'ZipCode_98122', 'ZipCode_98125', 'ZipCode_98133', 'ZipCode_98144', 'ZipCode_98199']
Liste des variables éliminées :
s[s==0].index.tolist()
['Proportion_Electricity', 'YearsENERGYSTARCertified_2008', 'YearsENERGYSTARCertified_2009', 'YearsENERGYSTARCertified_2014', 'ListOfAllPropertyUseTypes_Distribution Center', 'ListOfAllPropertyUseTypes_Medical Office', 'ListOfAllPropertyUseTypes_Multifamily Housing', 'ListOfAllPropertyUseTypes_Restaurant', 'Neighborhood_BALLARD', 'Neighborhood_LAKE UNION', 'Neighborhood_MAGNOLIA / QUEEN ANNE', 'Neighborhood_SOUTHEAST', 'CouncilDistrictCode_3', 'CouncilDistrictCode_5', 'CouncilDistrictCode_6', 'PrimaryPropertyType_Distribution Center', 'PrimaryPropertyType_Hotel', 'PrimaryPropertyType_Laboratory', 'PrimaryPropertyType_Low-Rise Multifamily', 'PrimaryPropertyType_Mixed Use Property', 'PrimaryPropertyType_Refrigerated Warehouse', 'PrimaryPropertyType_Residence Hall', 'PrimaryPropertyType_Restaurant', 'PrimaryPropertyType_Retail Store', 'PrimaryPropertyType_Small- and Mid-Sized Office', 'LargestPropertyUseType_Bank Branch', 'LargestPropertyUseType_College/University', 'LargestPropertyUseType_Hotel', 'LargestPropertyUseType_Other - Education', 'LargestPropertyUseType_Other - Mall', 'LargestPropertyUseType_Refrigerated Warehouse', 'LargestPropertyUseType_Social/Meeting Hall', 'LargestPropertyUseType_Worship Facility', 'SecondLargestPropertyUseType_K-12 School', 'SecondLargestPropertyUseType_Movie Theater', 'SecondLargestPropertyUseType_Multifamily Housing', 'SecondLargestPropertyUseType_Other - Entertainment/Public Assembly', 'SecondLargestPropertyUseType_Repair Services (Vehicle, Shoe, Locksmith, etc)', 'SecondLargestPropertyUseType_Self-Storage Facility', 'ThirdLargestPropertyUseType_Bank Branch', 'ThirdLargestPropertyUseType_Financial Office', 'ThirdLargestPropertyUseType_Fitness Center/Health Club/Gym', 'ThirdLargestPropertyUseType_Other', 'ThirdLargestPropertyUseType_Other - Entertainment/Public Assembly', 'ThirdLargestPropertyUseType_Parking', 'ThirdLargestPropertyUseType_Social/Meeting Hall', 'ZipCode_98103', 'ZipCode_98115', 'ZipCode_98121', 'ZipCode_98134']
Je filtre 'df' avec les colonnes selectionnées par LassoCV
et j'enregistre le résultat dans un nouveau DataFrame 'df_Energy'.
Je rajoute également à ce DataFrame la colonne 'ENERGYSTARScore' ainsi
que la colonne target qui lui correspond.
df_Energy = df[X.columns[s != 0].tolist()+['ENERGYSTARScore','SiteEnergyUse']]
print('Dimensions du DataFrame \'df_Energy\':',df_Energy.shape)
Dimensions du DataFrame 'df_Energy': (1425, 135)
Notre jeu de données a été nettoyé, encodé, standardisé et
est maintenant optimisé pour le Machine Learning.
Dans cette partie dans allons répondre aux 2 problématiques qui nous ont été posées :
Dans les deux cas, nous allons tester plusieurs algorithmes pour tenter de prédire,
avec le minimum d’erreur, l’émission de CO2 et la consommation totale d’énergie d’un bâtiment.
Comme il n’est pas possible, à priori, de savoir quel algorithme
saura le mieux généraliser notre problématique, et donc lequel nous donnera
les meilleurs résultats, je testerai et évaluerai différents algorithmes,
du plus simple au plus complexe.
Enfin, nous étudierons la pertinence d'utiliser la variable 'ENERGYSTARScore'.
Ainsi, je diviserai ce chapitre en 2 parties :
Voici dans l’ordre les algorithmes que nous allons tester :
Optimisation des Algorithmes :
Je réaliserai une recherche des meilleurs hyperparamètres
avec l’utilisation des modules GridSearchCV ou RandomizedSearchCV.
GridSearchCV permet d’exécuter et d’évaluer par validation croisée,
un modèle en testant 1 à 1 toutes les combinaisons d’hyperparamètres qu’on lui fournit.
GridSearchCV nous retournera alors la meilleure combinaison d’hyperparamètre
possible à utiliser en fonction des données d’entrainement qu’on lui a fourni.
RandomizedSearchCV repose sur le même principe que GridSearchCV
à la différence qu’il ne va pas chercher à tester toutes les combinaisons possibles,
mais à effectuer une recherche limitée et aléatoire des hyperparamètres fournis.
Le nombre de tests à effectuer est défini par l’utilisateur et dépendra du temps de calcul requis.
Il est préférable d’utiliser ce module lorsque la recherche exhaustive
de tous les hyperparamètres ne peut pas être réalisée sans une puissance
de calcul ou un temps raisonnable.
Evaluation des algorithmes
J’utiliserai 4 métriques pour juger de la qualité des algorithmes
et pour comparer entre eux leurs performances :
Pour rappel :
pickle.dump(df_Total, open('dfTotalBeformeML', 'wb'))
pickle.dump(df_Energy, open('dfEnergyBeformeML', 'wb'))
# df_Total = pickle.load(open('dfTotalBeformeML', 'rb'))
# df_Energy = pickle.load(open('dfEnergyBeformeML', 'rb'))
J'affiche et j'exporte dans un dictionnaire les résultats
de mes modèles via ma fonction displayMetrics()
Ma fonction displayBestAlgo() permet d'afficher et de mettre
en évidence (selon la métrique choisie) le meilleur algorithme
de Machine Learning parmi les algorithmes testés qui lui ont
été fournis dans le dictionnaire dictAlgoScore.
Déclaration de mes deux fonctions de gestion
d'affichage des scores et des meilleurs algorithmes :
def displayMetrics(model, X, y, pc1=-1, pc2=-1):
'''Affiche les métriques de l'algorithme testé.
Affiche également en secondes le temps de process
qui a été requis pour entraîner l'algorithme'''
# Calcul des métrics
R2 = round(r2_score(y, model.predict(X)),2)
MAE = round(mean_absolute_error(y, model.predict(X)),2)
MSE = round(mean_squared_error(y, model.predict(X)),2)
RMSE = round(mean_squared_error(y, model.predict(X), squared=False),2)
PC = round(pc2-pc1,2)
# Affichage des métrics
print('#'*50)
print(f'{"R²":-<45}{R2}')
print(f'{" Mean Absolute Error":-<45}{MAE}')
print(f'{" Mean Square Error":-<45}{MSE}')
print(f'{"Root Mean Square Error":-<45}{RMSE}')
if (pc1 != -1) & (pc2 != -1):
print(f'{"Process Time":-<45}{PC}s')
print('#'*50)
# On retourne les résultat sous forme de dictionnaire
return {'R²':R2, 'MAE':MAE, 'MSE':MSE, 'RMSE':RMSE, 'EXECUTION-TIME':PC}
def displayBestAlgo(dictListScore, importantMetric):
'''Affiche les resultats des différents algorithmes testés.
L'ordre d'affichage dépend de la métric passée dans <importantMetric>.
Les algorithmes sont affichés dans l'ordre du plus au moins performant.
Il est également possible d'afficher les algorithmes selon leur vitesse d'entraînement.
'''
if importantMetric == 'R²':
inv=True
else:
inv=False
print('#'*75)
print(f'{"Classement des différents algorithmes testés selon la métric:":-<70} {importantMetric}')
print('Les algorithmes testés sont affichées du plus au moins performant.')
print('#'*50)
for indice, i in enumerate(sorted(dictListScore.items(),
key = lambda x: x[1][importantMetric],
reverse=inv)):
print(f'{indice+1} {i[0]:-<37} {importantMetric}: {i[1][importantMetric]}') # Premier element du Tupple qui contient le nom de l'algorithme testé
print('#'*50)
for i in sorted(dictListScore.items(),
key = lambda x: x[1][importantMetric],
reverse=inv):
print(f'{"Algorithme testé":-<30} {i[0]}')
print(f'{"R²":-<45}{i[1]["R²"]}')
print(f'{" Mean Absolute Error":-<45}{i[1]["MAE"]}')
print(f'{" Mean Square Error":-<45}{i[1]["MSE"]}')
print(f'{"Root Mean Square Error":-<45}{i[1]["RMSE"]}')
print(f'{"Execution Time":-<45}{i[1]["EXECUTION-TIME"]}')
print('#'*50)
print('#'*75)
def ShowYTrueYPredError(modelML, X_true, y_true):
plt.scatter(y_true,
y_true,
color='firebrick',
alpha=0.7)
plt.scatter(
pd.DataFrame({'y_true':y_true,'y_pred':modelML.predict(X_true)}).sort_values(by='y_true')['y_true'],
pd.DataFrame({'y_true':y_true,'y_pred':modelML.predict(X_true)}).sort_values(by='y_true')['y_pred'],
color='dodgerblue',
alpha=0.7)
plt.show()
Je remplace le contenu de 'df' par le contenu de 'df_Total',
le DataFrame filtré et optimisé pour la target 'TotalGHGEmissions'.
df = df_Total
dictAlgoScore = {}
X = df.drop(['TotalGHGEmissions','ENERGYSTARScore'], axis=1)
y = df.TotalGHGEmissions
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=RS)
dummy_regr = DummyRegressor(strategy='mean')
pcStart = perf_counter()
dummy_regr.fit(X,y)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['DummyRegressor'] = displayMetrics(dummy_regr, X_test, y_test, pcStart, pcEnd)
##################################################
R²--------------------------------------------0.0
Mean Absolute Error---------------------1.49
Mean Square Error-----------------------3.54
Root Mean Square Error-----------------------1.88
Process Time---------------------------------0.0s
##################################################
Affichage des prédictions '**y_pred**' en bleu en fonction
de la valeur réelle de y '**y_true**' en rouge :
ShowYTrueYPredError(dummy_regr, X_test, y_test)
J'enregistre également la MSE de ma baseline afin de pouvoir l'afficher
dans des graphiques où on affichera l'erreur des algorithmes en fonction de certains paramètres :
baseline_error = mean_squared_error(y_test, dummy_regr.predict(X_test))
Version Linéaire de l'algorithme des K plus proches voisins.
Je test l'algortithme avec différentes valeurs de k,
allant de k = 1 à k = 49.
Je procède cette fois-ci à une cross-validation.
J'affiche ensuite le score du train-set et du validation-set en fonction de k.
knn = KNeighborsRegressor()
k = np.arange(1, 50)
train_score, val_score = validation_curve(knn,
X_train,
y_train,
'n_neighbors',
k,
cv=5)
plt.plot(k,val_score.mean(axis=1), label='validation')
plt.plot(k, train_score.mean(axis=1), label='train')
plt.ylabel('score')
plt.xlabel('n_neighbors')
plt.legend()
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\utils\validation.py:67: FutureWarning: Pass param_name=n_neighbors, param_range=[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
49] as keyword args. From version 0.25 passing these as positional arguments will result in an error
warnings.warn("Pass {} as keyword args. From version 0.25 "
<matplotlib.legend.Legend at 0x2517085d9d0>
On observe un maximum pour le score du validation-set entre k=15 et k=17.
Je réalise maintenant une recherche des meilleurs *hyperparamètres*
avec **GridSearchCV** en testant les autres *hyperparamètres* existants :
param_grid = {'n_neighbors': np.arange(1,20),
'metric': ['euclidean', 'manhattan'],
'weights': ['uniform', 'distance']}
grid = GridSearchCV(KNeighborsRegressor(),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
knn_opti = grid.best_estimator_
Meilleurs paramètres: {'metric': 'euclidean', 'n_neighbors': 11, 'weights': 'distance'}
Meilleur score: 0.4538680630959376
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KNeighborsRegressor'] = displayMetrics(knn_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.46
Mean Absolute Error---------------------1.05
Mean Square Error-----------------------1.9
Root Mean Square Error-----------------------1.38
Process Time---------------------------------17.7s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(knn_opti, X_test, y_test)
La régression Ridge est un régression linéaire avec une contrainte quadratique sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Ridge va appliquer des pénalités aux variables corrélées
et va appliquer aux variables corrélées le même poids à chacune d'entre-elles.
De cette façon, les variables corrélées n'auront pas plus d'influence
qu'une variable seule non corrélée avec les autres variables du jeu de données.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Ridge avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
ridge = Ridge(random_state=RS)
n_alphas = 1000
alphas = np.logspace(-5,6, n_alphas)
coefs = []
errors = []
for a in alphas:
ridge.set_params(alpha=a)
ridge.fit(X_train, y_train)
coefs.append(ridge.coef_)
errors.append(np.mean((ridge.predict(X_test) - y_test) ** 2))
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**6], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
0
errors[np.argmin(errors)]
0.7777750078238473
alphas[np.argmin(errors)]
1e-05
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 200)}
grid = GridSearchCV(Ridge(random_state=RS),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
ridge_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 0.2800503894183625}
Meilleur score: 0.6977004503260751
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Ridge'] = displayMetrics(ridge_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.76
Mean Absolute Error---------------------0.71
Mean Square Error-----------------------0.85
Root Mean Square Error-----------------------0.92
Process Time---------------------------------5.63s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(ridge_opti, X_test, y_test)
La régression Lasso est une régression linéaire avec une contrainte linéaire sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Lasso va appliquer des pénalités aux variables corrélées
et ne conserver qu'une seule variable parmi les variables corrélées
et passer le coefficiant des autres variables corrélées à 0.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Lasso avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
n_alphas = 300
alphas = np.logspace(-5, 1, n_alphas)
lasso = Lasso(fit_intercept=False,
random_state=RS)
coefs = []
errors = []
for a in alphas:
lasso.set_params(alpha=a)
lasso.fit(X_train, y_train)
coefs.append(lasso.coef_)
errors.append(np.mean((lasso.predict(X_test) - y_test) ** 2))
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.0124366339478, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.0497888528521, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.0888711468601, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.1297731413298, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.1725700716144, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.2173540847281, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.2642117994079, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.3132536730259, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.3645735844217, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.418271811602, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.4744375184434, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.5331899994751, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.5946405788922, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.6589001167355, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.7261194568674, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.796406888602, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.8698998737974, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.946749584824, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.0270788779326, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.1106096741511, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.1967051261946, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.2865379345855, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.380814294874, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.4799199172522, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.5839646246209, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.6931013655399, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.8074491801926, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.9271082559297, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.0522603675829, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.1830683475658, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.3197160586502, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.4624002350037, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.6113453253115, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.7667526154171, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.9288729857117, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 620.097981402792, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 620.2742712348298, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 620.458049085735, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 620.6495538390753, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 620.8490051795008, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 621.0562706524586, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 621.2723471992741, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 621.4973554580613, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 621.7314786890379, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 621.9750949141788, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 622.2283845878462, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 622.4916767408755, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 622.7651616894561, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 623.0491613418408, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 623.3443526360817, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 623.650621187487, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 623.9681480868471, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 624.2970764814071, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 624.637642126687, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 624.979977902753, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 625.3022256470532, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 625.6210198163776, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 625.9430887513147, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 626.2743770446046, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 626.6259756052278, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 626.9929627049252, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 627.374318934272, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 627.7685908619536, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 628.1740475993804, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 628.5887139990919, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 629.0112878052141, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 629.4396374226644, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 629.8724643919488, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 630.3075969717581, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 630.7429163474409, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 631.1757538007187, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 631.595133582341, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 632.0049407380327, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 632.1915648540203, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 632.2468459521883, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 632.0305913127027, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 631.6971715077468, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 631.3954657018228, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 631.0783628635677, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 630.7242694165238, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 630.3070618478274, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 629.7926524524727, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 629.0924104714908, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 628.230621616134, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 627.1838183433332, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 625.9044970942692, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 624.3896247297295, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 622.5689430152534, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 620.4107760395292, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.8238818125093, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 614.717804403766, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.9990918603467, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 606.3963009339158, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 601.0365122092746, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 594.6615531735591, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 586.9758773467101, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 578.1437449183136, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 567.5721449114078, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 555.0571367841039, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 539.1974446781719, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 519.1913174753317, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 515.2927595512076, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 520.7364376646065, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 526.0044827932928, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 531.0136415988064, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 535.8420659594059, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 540.5917871242268, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 545.1276466784558, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 543.4622275629418, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 538.2498459098906, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 532.1400482838906, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 525.2309418383761, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 518.3356025513465, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 510.96167482574214, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 503.1451485442881, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 494.7670913367492, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 485.97027961145756, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 476.4038365568808, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 466.55159206821185, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 455.4582815614065, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 443.6199107699413, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 431.48225263418925, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 418.8477684107258, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 404.85957882395553, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 391.0904746221089, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 376.55777047388193, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 361.47423669516706, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 344.29735638053114, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 326.3090954332385, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 308.4282302222017, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 289.53995471704525, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 270.0320423307652, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249.79943360866469, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 229.07490902629218, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 207.77515409702642, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 185.8987459268102, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 163.59290113400084, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 140.7519176584981, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 117.88919223746097, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 95.42552306457787, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 73.81347700124684, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53.20181622804671, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34.110107889695655, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.501470617743507, tolerance: 2.7746509182127324 model = cd_fast.enet_coordinate_descent(
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**1], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
124
errors[np.argmin(errors)]
1.3923724454896795
alphas[np.argmin(errors)]
0.00307818214256508
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.xlabel('alpha')
plt.ylabel('weights')
plt.axis('tight')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 300)}
grid = GridSearchCV(Lasso(random_state=RS), param_grid, cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
lasso_opti = grid.best_estimator_
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.7305528170422235, tolerance: 0.3178511728070718 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1294012058710905, tolerance: 0.32966237050977754 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25.985163322714982, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.780527031822885, tolerance: 0.3178511728070718 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1474572986913927, tolerance: 0.32966237050977754 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.055633228051818, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9052980097628733, tolerance: 0.3178511728070718 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.168352719108043, tolerance: 0.32966237050977754 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.133762985221267, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5443549287775795, tolerance: 0.3178511728070718 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.192525550343362, tolerance: 0.32966237050977754 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.220515956896236, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.153691816302569, tolerance: 0.3178511728070718 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.220069004674656, tolerance: 0.32966237050977754 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.316974637599685, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0186794955736218, tolerance: 0.3178511728070718 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.250258309334413, tolerance: 0.32966237050977754 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.424445088051243, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6872914768591727, tolerance: 0.3178511728070718 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.778926139184762, tolerance: 0.32966237050977754 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.54492381565956, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.584040564612792, tolerance: 0.3178511728070718 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0641162473306167, tolerance: 0.32966237050977754 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.678559954267314, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0040546600558855, tolerance: 0.3178511728070718 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.42112103894589836, tolerance: 0.32966237050977754 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.823231142534667, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.976114767482386, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27.15492866658178, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27.35266312451347, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27.570942161402797, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27.81144566240056, tolerance: 0.3338416367293735 model = cd_fast.enet_coordinate_descent(
Meilleurs paramètres: {'alpha': 0.002872063117797828}
Meilleur score: 0.7302890543001948
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Lasso'] = displayMetrics(lasso_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.78
Mean Absolute Error---------------------0.68
Mean Square Error-----------------------0.79
Root Mean Square Error-----------------------0.89
Process Time---------------------------------16.22s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(lasso_opti, X_test, y_test)
Les algorithmes Support Vector Machine sont généralement utilisés dans des problèmes de classification.
L'idée de cet algorithme est de trouver un hyperplan qui divise notre jeu de données en deux.
SVR est la version Regression du Support Vector Machine (SVM).
Cet algorithme fonctionne bien sur de petits dataset.
Son entrainement peut-être long et il est sensible aux Outliers.
Premier essai avec les paramètres par défaut :
svr = SVR()
svr.fit(X_train, y_train)
displayMetrics(svr, X_test, y_test)
##################################################
R²-------------------------------------------0.63
Mean Absolute Error---------------------0.9
Mean Square Error-----------------------1.29
Root Mean Square Error-----------------------1.13
##################################################
{'R²': 0.63, 'MAE': 0.9, 'MSE': 1.29, 'RMSE': 1.13, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **RandomizedSearchCV** :
J'utilise RandomizedSearchCV car la recherche des meilleurs hyperparamètres est particulièrement longue avec SVR
param_grid = {'kernel': ['linear','rbf'],
'degree': range(3,5),
'gamma' : np.logspace(-2, 1, 4),
'C': np.logspace(-3, 3, 7)}
grid = RandomizedSearchCV(SVR(),
param_grid,
cv=5,
scoring='r2',
n_iter = 10)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
svr_opti = grid.best_estimator_
Meilleurs paramètres: {'kernel': 'linear', 'gamma': 1.0, 'degree': 4, 'C': 10.0}
Meilleur score: 0.6674123054513694
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['SVR'] = displayMetrics(svr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.75
Mean Absolute Error---------------------0.71
Mean Square Error-----------------------0.88
Root Mean Square Error-----------------------0.94
Process Time---------------------------------16.36s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(svr_opti, X_test, y_test)
La Regression Ridge à noyau est une regression ridge
à laquelle nous appliquons l'astuce du noyau.
L'astuce du noyau (Kernel Trick) est une méthode qui permet
d'utiliser un classifieur linéaire pour résoudre un problème non linéaire.
L'idée est de transformer l'espace de représentation des données d'entrée
en un espace de plus grande dimension.
La discrimination linéaire dans l'espace de grande dimension
(appelé aussi espace de redescription) est équivalente à une discrimination
non linéaire dans l'espace d'origine.
Premier essai avec les paramètres par défaut :
kr = KernelRidge()
kr.fit(X_train, y_train)
displayMetrics(kr, X_test, y_test)
##################################################
R²-------------------------------------------0.67
Mean Absolute Error---------------------0.83
Mean Square Error-----------------------1.16
Root Mean Square Error-----------------------1.08
##################################################
{'R²': 0.67, 'MAE': 0.83, 'MSE': 1.16, 'RMSE': 1.08, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'kernel':['rbf','linear','poly'],
'degree':[3,4],
'alpha': np.logspace(-2, 2, 5),
'gamma': np.logspace(-2, 1, 4)}
grid = GridSearchCV(KernelRidge(),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
kr_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 0.01, 'degree': 3, 'gamma': 0.01, 'kernel': 'rbf'}
Meilleur score: 0.7028755311020701
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KernelRidge'] = displayMetrics(kr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.75
Mean Absolute Error---------------------0.72
Mean Square Error-----------------------0.87
Root Mean Square Error-----------------------0.93
Process Time---------------------------------15.54s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(kr_opti, X_test, y_test)
L'algorithme Random Forest fait partie des algorithmes qui utilisent
la méthode du bagging et fait donc partie des algorithmes ensemblistes.
Rappel sur le bagging :
Bagging est une contraction de Bootstrap Aggregation.
Le bagging a pour objectif de réduire la variance de l’estimateur (les problèmes liés
au surapprentissage, et donc qu'une petite modification en entrée (jeu de données) entraîne
de grandes différences en sortie).
L'idée est de créer plusieurs entités d'un même modèle
et d'entrainer chacune de ces entités sur une portion aléatoire
de notre jeu de données.
Pour cela on utilise une technique d'échantillonnage appelé Bootstrapping.
Cette technique consiste à replacer après chaque tirage au sort,
les données qui ont été sélectionnées dans notre jeu de données (on parle
alors de tirage avec remise).
De cette manière, on obtient un groupe de modèles diversifiés (ils n'ont
pas tous été nourris avec les mêmes données mais ils partagent cependant
certaines connaissances en commun) ce qui nous permet d’obtenir des majorités
en faveur des bonnes réponses.
On regroupe ensuite les résultats de chaque modèle pour faire notre prédiction finale.
Random Forest est l'algorithme le plus connu utilisant cette technique.
Il utilise comme modèle de base, l'arbre de décision.
Le nom de Random Forest est logiquement choisi comme tel car nous générons
des arbres de décision aléatoirement.
En cela nous créons donc une forêt aléatoire.
Entrainement avec les paramètres par défaut et n_estimator = 1000 :
rfr = RandomForestRegressor(n_estimators=1000,
n_jobs=-1,
random_state=RS)
pcStart = perf_counter()
rfr.fit(X_train, y_train)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['RandomForestRegressor'] = displayMetrics(rfr, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.76
Mean Absolute Error---------------------0.68
Mean Square Error-----------------------0.84
Root Mean Square Error-----------------------0.92
Process Time---------------------------------1.49s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(rfr, X_test, y_test)
AdaBoost est une méthode de Boosting et fait donc
également partie des algorithmes ensemblistes.
L'idée est d'entraîner l'un après l'autre plusieurs modèle relativement faibles
en demandant à chaque modèle d'essayer de corriger les erreurs effectuées par son prédecesseur.
On obtient ainsi un ensemble de modèles complèmentaires dans lequel
les faiblesses des uns sont compensées par les forces des autres.
Ici les modèles sont entraînés en série.
Chaque modèle est en situation d'underfitting mais en les construisant
les uns par dessus les autres, on est capable de réduire le biais général de tous ces modèles.
Adaboost est un algorithme de boosting qui s’appuie sur ce principe,
avec un paramètre de mise à jour adaptatif permettant de donner plus
d’importance aux valeurs difficiles à prédire, donc en boostant les régresseurs
qui réussissent quand d’autres ont échoué.
Entrainement avec les paramètres par défaut :
adb = AdaBoostRegressor(random_state=RS)
adb.fit(X_train, y_train)
displayMetrics(adb, X_test, y_test)
##################################################
R²-------------------------------------------0.66
Mean Absolute Error---------------------0.83
Mean Square Error-----------------------1.21
Root Mean Square Error-----------------------1.1
##################################################
{'R²': 0.66, 'MAE': 0.83, 'MSE': 1.21, 'RMSE': 1.1, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {"base_estimator__max_depth" : np.linspace(10,100,10),
"n_estimators": [100, 200, 300]
}
DTR = DecisionTreeRegressor(random_state=RS)
ABR = AdaBoostRegressor(base_estimator = DTR,
random_state=RS)
grid = GridSearchCV(ABR,
param_grid=param_grid,
scoring = 'r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
adb_opti = grid.best_estimator_
Meilleurs paramètres: {'base_estimator__max_depth': 30.0, 'n_estimators': 200}
Meilleur score: 0.7197398024618057
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['AdaBoostRegressor'] = displayMetrics(adb_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.78
Mean Absolute Error---------------------0.65
Mean Square Error-----------------------0.79
Root Mean Square Error-----------------------0.89
Process Time---------------------------------288.71s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(adb_opti, X_test, y_test)
Comme son nom l'indique, GradientBoosting est un algorithme de boosting.
En celà il reprend le même principe de fonctionnement qu'AdaBoost.
GradiantBoosting diffère d'AdaBoost sur sa fonction de perte,
qui s'appuie sur la descente de gradient.
La perte représente la pénalité générée lorsque l'estimation par le modèle
n'est pas parfaitement égale à la cible.
Une fonction de perte quantifie cette pénalité sous la forme d'une valeur individuelle.
La descente de gradient est un algorithme qui permet de trouver le minimum d’une fonction.
Le calcul de la dérivée en un point d'une fonction nous permet de définir
la pente de cette fonction et donc de définir le sens dans lequel il faut
se déplacer sur cette fonction pour se rapprocher de son minimum (local).
Entrainement avec les paramètres par défaut :
gbr = GradientBoostingRegressor(random_state=RS)
gbr.fit(X_train, y_train)
displayMetrics(gbr, X_test, y_test)
##################################################
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.63
Mean Square Error-----------------------0.7
Root Mean Square Error-----------------------0.84
##################################################
{'R²': 0.8, 'MAE': 0.63, 'MSE': 0.7, 'RMSE': 0.84, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'learning_rate': [0.1,0.2,0.3],
'n_estimators':[100,200,300,400,500],
'subsample': [0.4,0.5,0.6,1],
'max_depth': [2,3]}
grid = GridSearchCV(GradientBoostingRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
gbr_opti = grid.best_estimator_
Meilleurs paramètres: {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 300, 'subsample': 0.6}
Meilleur score: 0.7860260259456873
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['GradientBoostingRegressor'] = displayMetrics(gbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.81
Mean Absolute Error---------------------0.63
Mean Square Error-----------------------0.66
Root Mean Square Error-----------------------0.81
Process Time---------------------------------270.79s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(gbr_opti, X_test, y_test)
XGBoost signifie Extreme Gradient Boosting.
Il est assez similaire à l'algorithme GradientBoosting.
Il s'agit d'une implémentation spécifique du modèle Gradient Boosting
qui utilise des approximations plus précises pour trouver
le meilleur modèle d'arbre de décision.
Les deux algorithmes suivent le principe de descente de gradient.
XGBoost est plus rapide à l'exécution que GradientBoosting.
Liste non exaustive des avantages de XGBoost sur GradientBoosting :
Entrainement avec les paramètres par défaut :
xgbr = XGBRegressor(random_state=RS)
xgbr.fit(X_train, y_train)
displayMetrics(xgbr, X_test, y_test)
##################################################
R²-------------------------------------------0.78
Mean Absolute Error---------------------0.66
Mean Square Error-----------------------0.76
Root Mean Square Error-----------------------0.87
##################################################
{'R²': 0.78, 'MAE': 0.66, 'MSE': 0.76, 'RMSE': 0.87, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'eta':[0.1,0.2],
'gamma':np.linspace(0,0.2,3),
'max_depth': range(2,6,2),
'subsample': np.linspace(0.5,0.8,4),
'colsample_bytree': np.linspace(0.5,0.8,4)
}
grid = GridSearchCV(XGBRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2'
)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
xgbr_opti = grid.best_estimator_
Meilleurs paramètres: {'colsample_bytree': 0.7000000000000001, 'eta': 0.1, 'gamma': 0.0, 'max_depth': 4, 'subsample': 0.8}
Meilleur score: 0.7750880844318286
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['XGBRegressor'] = displayMetrics(xgbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.81
Mean Absolute Error---------------------0.62
Mean Square Error-----------------------0.66
Root Mean Square Error-----------------------0.81
Process Time---------------------------------52.91s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(xgbr_opti, X_test, y_test)
displayBestAlgo(dictAlgoScore, 'R²')
###########################################################################
Classement des différents algorithmes testés selon la métric:--------- R²
Les algorithmes testés sont affichées du plus au moins performant.
##################################################
1 GradientBoostingRegressor------------ R²: 0.81
2 XGBRegressor------------------------- R²: 0.81
3 Lasso-------------------------------- R²: 0.78
4 AdaBoostRegressor-------------------- R²: 0.78
5 Ridge-------------------------------- R²: 0.76
6 RandomForestRegressor---------------- R²: 0.76
7 SVR---------------------------------- R²: 0.75
8 KernelRidge-------------------------- R²: 0.75
9 KNeighborsRegressor------------------ R²: 0.46
10 DummyRegressor----------------------- R²: -0.0
##################################################
Algorithme testé-------------- GradientBoostingRegressor
R²-------------------------------------------0.81
Mean Absolute Error---------------------0.63
Mean Square Error-----------------------0.66
Root Mean Square Error-----------------------0.81
Execution Time-------------------------------270.79
##################################################
Algorithme testé-------------- XGBRegressor
R²-------------------------------------------0.81
Mean Absolute Error---------------------0.62
Mean Square Error-----------------------0.66
Root Mean Square Error-----------------------0.81
Execution Time-------------------------------52.91
##################################################
Algorithme testé-------------- Lasso
R²-------------------------------------------0.78
Mean Absolute Error---------------------0.68
Mean Square Error-----------------------0.79
Root Mean Square Error-----------------------0.89
Execution Time-------------------------------16.22
##################################################
Algorithme testé-------------- AdaBoostRegressor
R²-------------------------------------------0.78
Mean Absolute Error---------------------0.65
Mean Square Error-----------------------0.79
Root Mean Square Error-----------------------0.89
Execution Time-------------------------------288.71
##################################################
Algorithme testé-------------- Ridge
R²-------------------------------------------0.76
Mean Absolute Error---------------------0.71
Mean Square Error-----------------------0.85
Root Mean Square Error-----------------------0.92
Execution Time-------------------------------5.63
##################################################
Algorithme testé-------------- RandomForestRegressor
R²-------------------------------------------0.76
Mean Absolute Error---------------------0.68
Mean Square Error-----------------------0.84
Root Mean Square Error-----------------------0.92
Execution Time-------------------------------1.49
##################################################
Algorithme testé-------------- SVR
R²-------------------------------------------0.75
Mean Absolute Error---------------------0.71
Mean Square Error-----------------------0.88
Root Mean Square Error-----------------------0.94
Execution Time-------------------------------16.36
##################################################
Algorithme testé-------------- KernelRidge
R²-------------------------------------------0.75
Mean Absolute Error---------------------0.72
Mean Square Error-----------------------0.87
Root Mean Square Error-----------------------0.93
Execution Time-------------------------------15.54
##################################################
Algorithme testé-------------- KNeighborsRegressor
R²-------------------------------------------0.46
Mean Absolute Error---------------------1.05
Mean Square Error-----------------------1.9
Root Mean Square Error-----------------------1.38
Execution Time-------------------------------17.7
##################################################
Algorithme testé-------------- DummyRegressor
R²--------------------------------------------0.0
Mean Absolute Error---------------------1.49
Mean Square Error-----------------------3.54
Root Mean Square Error-----------------------1.88
Execution Time-------------------------------0.0
##################################################
###########################################################################
df = df_Energy
dictAlgoScore = {}
X = df.drop(['SiteEnergyUse','ENERGYSTARScore'], axis=1)
y = df.SiteEnergyUse
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=RS)
dummy_regr = DummyRegressor(strategy='mean')
pcStart = perf_counter()
dummy_regr.fit(X,y)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['DummyRegressor'] = displayMetrics(dummy_regr, X_test, y_test, pcStart, pcEnd)
##################################################
R²--------------------------------------------0.0
Mean Absolute Error---------------------1.36
Mean Square Error-----------------------2.85
Root Mean Square Error-----------------------1.69
Process Time---------------------------------0.0s
##################################################
Affichage des prédictions '**y_pred**' en bleu en fonction
de la valeur réelle de y '**y_true**' en rouge :
ShowYTrueYPredError(dummy_regr, X_test, y_test)
J'enregistre également la MSE de ma baseline afin de pouvoir l'afficher
dans des graphiques où on affichera l'erreur des algorithmes en fonction de certains paramètres :
baseline_error = mean_squared_error(y_test, dummy_regr.predict(X_test))
Version Linéaire de l'algorithme des K plus proches voisins.
Je test l'algortithme avec différentes valeurs de k,
allant de k = 1 à k = 49.
Je procède cette fois-ci à une cross-validation.
J'affiche ensuite le score du train-set et du validation-set en fonction de k.
knn = KNeighborsRegressor()
k = np.arange(1, 50)
train_score, val_score = validation_curve(knn,
X_train,
y_train,
'n_neighbors',
k,
cv=5)
plt.plot(k,val_score.mean(axis=1), label='validation')
plt.plot(k, train_score.mean(axis=1), label='train')
plt.ylabel('score')
plt.xlabel('n_neighbors')
plt.legend()
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\utils\validation.py:67: FutureWarning: Pass param_name=n_neighbors, param_range=[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
49] as keyword args. From version 0.25 passing these as positional arguments will result in an error
warnings.warn("Pass {} as keyword args. From version 0.25 "
<matplotlib.legend.Legend at 0x251723a0250>
On observe un maximum pour le score du validation-set entre k=15 et k=17.
Je réalise maintenant une recherche des meilleurs *hyperparamètres*
avec **GridSearchCV** en testant les autres *hyperparamètres* existants :
param_grid = {'n_neighbors': np.arange(1,20),
'metric': ['euclidean', 'manhattan'],
'weights': ['uniform', 'distance']}
grid = GridSearchCV(KNeighborsRegressor(),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
knn_opti = grid.best_estimator_
Meilleurs paramètres: {'metric': 'manhattan', 'n_neighbors': 19, 'weights': 'distance'}
Meilleur score: 0.3865348901629169
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KNeighborsRegressor'] = displayMetrics(knn_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.43
Mean Absolute Error---------------------0.96
Mean Square Error-----------------------1.62
Root Mean Square Error-----------------------1.27
Process Time---------------------------------15.56s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(knn_opti, X_test, y_test)
La régression Ridge est un régression linéaire avec une contrainte quadratique sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Ridge va appliquer des pénalités aux variables corrélées
et va appliquer aux variables corrélées le même poids à chacune d'entre-elles.
De cette façon, les variables corrélées n'auront pas plus d'influence
qu'une variable seule non corrélée avec les autres variables du jeu de données.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Ridge avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
ridge = Ridge(random_state=RS)
n_alphas = 1000
alphas = np.logspace(-5,6, n_alphas)
coefs = []
errors = []
for a in alphas:
ridge.set_params(alpha=a)
ridge.fit(X_train, y_train)
coefs.append(ridge.coef_)
errors.append(np.mean((ridge.predict(X_test) - y_test) ** 2))
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**6], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
0
errors[np.argmin(errors)]
0.7760685932470769
alphas[np.argmin(errors)]
1e-05
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 200)}
grid = GridSearchCV(Ridge(random_state=RS),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
ridge_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 1e-05}
Meilleur score: 0.6439029488861758
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Ridge'] = displayMetrics(ridge_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.73
Mean Absolute Error---------------------0.65
Mean Square Error-----------------------0.78
Root Mean Square Error-----------------------0.88
Process Time---------------------------------5.4s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(ridge_opti, X_test, y_test)
La régression Lasso est une régression linéaire avec une contrainte linéaire sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Lasso va appliquer des pénalités aux variables corrélées
et ne conserver qu'une seule variable parmi les variables corrélées
et passer le coefficiant des autres variables corrélées à 0.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Lasso avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
n_alphas = 300
alphas = np.logspace(-5, 1, n_alphas)
lasso = Lasso(fit_intercept=False,
random_state=RS)
coefs = []
errors = []
for a in alphas:
lasso.set_params(alpha=a)
lasso.fit(X_train, y_train)
coefs.append(lasso.coef_)
errors.append(np.mean((lasso.predict(X_test) - y_test) ** 2))
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.4166912886325, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.4400106637506, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.4644139300292, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.4899516842421, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.5166894754724, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.5446589030985, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.5739401137672, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.6045634047148, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.6366206008264, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.670141079445, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.7052090888561, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.7418942547189, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.7802730486178, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.8204593561504, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.8624483408588, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.9063611561215, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.952284737841, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.0003082024626, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.0505823386108, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.1031155211201, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.1580107094978, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.215460231904, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.2755021249199, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.3382287003438, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.4037839063845, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.4722903179456, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.5438886498192, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.6186629910195, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.6968182855128, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.7784316022213, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.8635958720436, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 610.952614646924, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 611.0455303728389, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 611.1425323822616, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 611.2436357832365, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 611.3490653252412, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 611.4590777837883, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 611.5739105609999, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 611.6936998295556, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 611.8186075284361, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 611.9487914358438, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 612.0844699892311, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 612.2258824629673, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 612.3731528238831, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 612.5264955057838, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 612.6860567458531, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 612.8521598830125, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 613.0249165869509, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 613.2044714436937, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 613.3911611959668, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 613.5851460113082, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 613.7865963752406, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 613.9955787287545, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 614.2125432749036, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 614.4375519752485, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 614.6706012919482, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 614.9123762691593, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 615.1631906488903, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 615.4234954878772, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 615.6949647484594, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 615.9802976948499, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 616.2387337368987, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 616.483641636504, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 616.7299409643618, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 616.976699161686, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.2274076658075, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.476926275076, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.7227887649465, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.9650338598035, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.2100806532193, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.4331801256193, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.6404057961036, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 618.8360891353215, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.0125573957725, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.165959305315, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.3050905889833, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.5108371291634, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.7384363104276, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 619.9466914048587, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 620.1319576449172, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 620.2872288098057, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 620.4046654310096, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 620.4813241902943, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 620.5400091280383, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 617.59312102419, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 615.1066520919751, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 613.3368802501943, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 611.5867084586741, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 609.6102178240969, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 607.1702450171778, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 604.3403407989352, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 601.0166343958844, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 597.0540708439919, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 593.5777176664878, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 589.0883006988936, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 584.2271989101922, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 578.6581851933536, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 572.1569365512006, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 564.4980867866127, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 555.4439951945296, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 544.6646384755171, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 531.7701034377537, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 516.2483596411384, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 497.46376876004757, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 497.4187820795647, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 501.43145376260145, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 505.11954332161656, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 508.4042487865717, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 508.01456902072016, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 506.59487296790627, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 505.9304337381186, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 504.6553008283395, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 503.30196970928137, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 500.8272945328587, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 498.43854936871537, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 496.75463045549355, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 491.8319237733259, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 481.1639486657279, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 472.0375685690745, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 461.87722685248497, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 451.4993909912346, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 440.3548727809907, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 428.67544335732265, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 416.17959624171976, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 403.50053196765145, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 389.89788129861347, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 375.5929057045237, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 359.8506934327659, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 342.56884096904855, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 324.10896070237123, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 306.3685789556205, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 287.9549876378077, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 268.86287044211906, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 248.6732895890002, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 228.586252177559, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 208.33832478417446, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 188.1949858906911, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168.00345283938532, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 148.45173423037295, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129.76022383638747, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 112.4121344428977, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 97.52735330014298, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81.78378226227369, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81.75336897359693, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81.96486974736627, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 82.17540268536368, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 82.37750556931769, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 82.54018707711816, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 82.69848111753004, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 82.83618631548438, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.00585268870282, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.4166400910708, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.50692824648854, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.57303486603405, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.67977053410436, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.70548358058409, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.70406189543678, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.8368333186022, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.55470043162018, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.61156310207025, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.61694677004039, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 84.09722044181763, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 84.03770940496543, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 84.1415301853308, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.99538921324643, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.77987296634501, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.45746268225548, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83.03823269137706, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 82.5035581822458, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81.91120045233333, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81.06612940965624, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 80.23285203181422, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79.23709961496229, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 77.97416729973725, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 76.5966348874565, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 74.96499505220345, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 73.13472093585392, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 71.0556131621554, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 68.59903767017249, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65.54428082040135, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62.52528715687299, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59.09144939879366, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.24197634893153, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50.927815743844576, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46.065752348014485, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40.69327137349683, tolerance: 35.389349786450424 model = cd_fast.enet_coordinate_descent(
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**1], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
123
errors[np.argmin(errors)]
1.3242154840648865
alphas[np.argmin(errors)]
0.0029391883986468873
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.xlabel('alpha')
plt.ylabel('weights')
plt.axis('tight')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 300)}
grid = GridSearchCV(Lasso(random_state=RS), param_grid, cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
lasso_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 0.00227960463731898}
Meilleur score: 0.6522384161996516
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Lasso'] = displayMetrics(lasso_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.71
Mean Absolute Error---------------------0.67
Mean Square Error-----------------------0.82
Root Mean Square Error-----------------------0.9
Process Time---------------------------------11.51s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(lasso_opti, X_test, y_test)
Les algorithmes Support Vector Machine sont généralement utilisés dans des problèmes de classification.
L'idée de cet algorithme est de trouver un hyperplan qui divise notre jeu de données en deux.
SVR est la version Regression du Support Vector Machine (SVM).
Cet algorithme fonctionne bien sur de petits dataset.
Son entrainement peut-être long et il est sensible aux Outliers.
Premier essai avec les paramètres par défaut :
svr = SVR()
svr.fit(X_train, y_train)
displayMetrics(svr, X_test, y_test)
##################################################
R²-------------------------------------------0.55
Mean Absolute Error---------------------0.89
Mean Square Error-----------------------1.27
Root Mean Square Error-----------------------1.13
##################################################
{'R²': 0.55, 'MAE': 0.89, 'MSE': 1.27, 'RMSE': 1.13, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **RandomizedSearchCV** :
J'utilise RandomizedSearchCV car la recherche des meilleurs hyperparamètres est particulièrement longue avec SVR
param_grid = {'kernel': ['linear','rbf'],
'degree': range(3,5),
'gamma' : np.logspace(-2, 1, 4),
'C': np.logspace(-3, 3, 7)}
grid = RandomizedSearchCV(SVR(),
param_grid,
cv=5,
scoring='r2',
n_iter = 10)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
svr_opti = grid.best_estimator_
Meilleurs paramètres: {'kernel': 'linear', 'gamma': 10.0, 'degree': 4, 'C': 10.0}
Meilleur score: 0.6138844778881113
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['SVR'] = displayMetrics(svr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.7
Mean Absolute Error---------------------0.69
Mean Square Error-----------------------0.87
Root Mean Square Error-----------------------0.93
Process Time---------------------------------13.21s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(svr_opti, X_test, y_test)
La Regression Ridge à noyau est une regression ridge
à laquelle nous appliquons l'astuce du noyau.
L'astuce du noyau (Kernel Trick) est une méthode qui permet
d'utiliser un classifieur linéaire pour résoudre un problème non linéaire.
L'idée est de transformer l'espace de représentation des données d'entrée
en un espace de plus grande dimension.
La discrimination linéaire dans l'espace de grande dimension
(appelé aussi espace de redescription) est équivalente à une discrimination
non linéaire dans l'espace d'origine.
Premier essai avec les paramètres par défaut :
kr = KernelRidge()
kr.fit(X_train, y_train)
displayMetrics(kr, X_test, y_test)
##################################################
R²-------------------------------------------0.63
Mean Absolute Error---------------------0.78
Mean Square Error-----------------------1.07
Root Mean Square Error-----------------------1.03
##################################################
{'R²': 0.63, 'MAE': 0.78, 'MSE': 1.07, 'RMSE': 1.03, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'kernel':['rbf','linear','poly'],
'degree':[3,4],
'alpha': np.logspace(-2, 2, 5),
'gamma': np.logspace(-2, 1, 4)}
grid = GridSearchCV(KernelRidge(),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
kr_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 0.01, 'degree': 3, 'gamma': 0.01, 'kernel': 'rbf'}
Meilleur score: 0.6106094875182866
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KernelRidge'] = displayMetrics(kr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.69
Mean Absolute Error---------------------0.72
Mean Square Error-----------------------0.89
Root Mean Square Error-----------------------0.95
Process Time---------------------------------15.65s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(kr_opti, X_test, y_test)
L'algorithme Random Forest fait partie des algorithmes qui utilisent
la méthode du bagging et fait donc partie des algorithmes ensemblistes.
Rappel sur le bagging :
Bagging est une contraction de Bootstrap Aggregation.
Le bagging a pour objectif de réduire la variance de l’estimateur (les problèmes liés
au surapprentissage, et donc qu'une petite modification en entrée (jeu de données) entraîne
de grandes différences en sortie).
L'idée est de créer plusieurs entités d'un même modèle
et d'entrainer chacune de ces entités sur une portion aléatoire
de notre jeu de données.
Pour cela on utilise une technique d'échantillonnage appelé Bootstrapping.
Cette technique consiste à replacer après chaque tirage au sort,
les données qui ont été sélectionnées dans notre jeu de données (on parle
alors de tirage avec remise).
De cette manière, on obtient un groupe de modèles diversifiés (ils n'ont
pas tous été nourris avec les mêmes données mais ils partagent cependant
certaines connaissances en commun) ce qui nous permet d’obtenir des majorités
en faveur des bonnes réponses.
On regroupe ensuite les résultats de chaque modèle pour faire notre prédiction finale.
Random Forest est l'algorithme le plus connu utilisant cette technique.
Il utilise comme modèle de base, l'arbre de décision.
Le nom de Random Forest est logiquement choisi comme tel car nous générons
des arbres de décision aléatoirement.
En cela nous créons donc une forêt aléatoire.
Entrainement avec les paramètres par défaut et n_estimator = 1000 :
rfr = RandomForestRegressor(n_estimators=1000,
n_jobs=-1,
random_state=RS)
pcStart = perf_counter()
rfr.fit(X_train, y_train)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['RandomForestRegressor'] = displayMetrics(rfr, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.7
Mean Absolute Error---------------------0.67
Mean Square Error-----------------------0.84
Root Mean Square Error-----------------------0.92
Process Time---------------------------------1.41s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(rfr, X_test, y_test)
AdaBoost est une méthode de Boosting et fait donc
également partie des algorithmes ensemblistes.
L'idée est d'entraîner l'un après l'autre plusieurs modèle relativement faibles
en demandant à chaque modèle d'essayer de corriger les erreurs effectuées par son prédecesseur.
On obtient ainsi un ensemble de modèles complèmentaires dans lequel
les faiblesses des uns sont compensées par les forces des autres.
Ici les modèles sont entraînés en série.
Chaque modèle est en situation d'underfitting mais en les construisant
les uns par dessus les autres, on est capable de réduire le biais général de tous ces modèles.
Adaboost est un algorithme de boosting qui s’appuie sur ce principe,
avec un paramètre de mise à jour adaptatif permettant de donner plus
d’importance aux valeurs difficiles à prédire, donc en boostant les régresseurs
qui réussissent quand d’autres ont échoué.
Entrainement avec les paramètres par défaut :
adb = AdaBoostRegressor(random_state=RS)
adb.fit(X_train, y_train)
displayMetrics(adb, X_test, y_test)
##################################################
R²-------------------------------------------0.62
Mean Absolute Error---------------------0.77
Mean Square Error-----------------------1.09
Root Mean Square Error-----------------------1.04
##################################################
{'R²': 0.62, 'MAE': 0.77, 'MSE': 1.09, 'RMSE': 1.04, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {"base_estimator__max_depth" : np.linspace(10,100,10),
"n_estimators": [100, 200, 300]
}
DTR = DecisionTreeRegressor(random_state=RS)
ABR = AdaBoostRegressor(base_estimator = DTR,
random_state=RS)
grid = GridSearchCV(ABR,
param_grid=param_grid,
scoring = 'r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
adb_opti = grid.best_estimator_
Meilleurs paramètres: {'base_estimator__max_depth': 40.0, 'n_estimators': 300}
Meilleur score: 0.6570407888342772
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['AdaBoostRegressor'] = displayMetrics(adb_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.72
Mean Absolute Error---------------------0.63
Mean Square Error-----------------------0.8
Root Mean Square Error-----------------------0.9
Process Time---------------------------------279.99s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(adb_opti, X_test, y_test)
Comme son nom l'indique, GradientBoosting est un algorithme de boosting.
En celà il reprend le même principe de fonctionnement qu'AdaBoost.
GradiantBoosting diffère d'AdaBoost sur sa fonction de perte,
qui s'appuie sur la descente de gradient.
La perte représente la pénalité générée lorsque l'estimation par le modèle
n'est pas parfaitement égale à la cible.
Une fonction de perte quantifie cette pénalité sous la forme d'une valeur individuelle.
La descente de gradient est un algorithme qui permet de trouver le minimum d’une fonction.
Le calcul de la dérivée en un point d'une fonction nous permet de définir
la pente de cette fonction et donc de définir le sens dans lequel il faut
se déplacer sur cette fonction pour se rapprocher de son minimum (local).
Entrainement avec les paramètres par défaut :
gbr = GradientBoostingRegressor(random_state=RS)
gbr.fit(X_train, y_train)
displayMetrics(gbr, X_test, y_test)
##################################################
R²-------------------------------------------0.74
Mean Absolute Error---------------------0.63
Mean Square Error-----------------------0.74
Root Mean Square Error-----------------------0.86
##################################################
{'R²': 0.74, 'MAE': 0.63, 'MSE': 0.74, 'RMSE': 0.86, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'learning_rate': [0.1,0.2,0.3],
'n_estimators':[100,200,300,400,500],
'subsample': [0.4,0.5,0.6,1],
'max_depth': [2,3]}
grid = GridSearchCV(GradientBoostingRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
gbr_opti = grid.best_estimator_
Meilleurs paramètres: {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 500, 'subsample': 1}
Meilleur score: 0.717104175200129
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['GradientBoostingRegressor'] = displayMetrics(gbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.76
Mean Absolute Error---------------------0.61
Mean Square Error-----------------------0.69
Root Mean Square Error-----------------------0.83
Process Time---------------------------------254.88s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(gbr_opti, X_test, y_test)
XGBoost signifie Extreme Gradient Boosting.
Il est assez similaire à l'algorithme GradientBoosting.
Il s'agit d'une implémentation spécifique du modèle Gradient Boosting
qui utilise des approximations plus précises pour trouver
le meilleur modèle d'arbre de décision.
Les deux algorithmes suivent le principe de descente de gradient.
XGBoost est plus rapide à l'exécution que GradientBoosting.
Liste non exaustive des avantages de XGBoost sur GradientBoosting :
Entrainement avec les paramètres par défaut :
xgbr = XGBRegressor(random_state=RS)
xgbr.fit(X_train, y_train)
displayMetrics(xgbr, X_test, y_test)
##################################################
R²-------------------------------------------0.73
Mean Absolute Error---------------------0.65
Mean Square Error-----------------------0.77
Root Mean Square Error-----------------------0.88
##################################################
{'R²': 0.73, 'MAE': 0.65, 'MSE': 0.77, 'RMSE': 0.88, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'eta':[0.1,0.2],
'gamma':np.linspace(0,0.2,3),
'max_depth': range(2,6,2),
'subsample': np.linspace(0.5,0.8,4),
'colsample_bytree': np.linspace(0.5,0.8,4)
}
grid = GridSearchCV(XGBRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2'
)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
xgbr_opti = grid.best_estimator_
Meilleurs paramètres: {'colsample_bytree': 0.7000000000000001, 'eta': 0.1, 'gamma': 0.1, 'max_depth': 4, 'subsample': 0.8}
Meilleur score: 0.7135021530567534
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['XGBRegressor'] = displayMetrics(xgbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.75
Mean Absolute Error---------------------0.63
Mean Square Error-----------------------0.72
Root Mean Square Error-----------------------0.85
Process Time---------------------------------48.1s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(xgbr_opti, X_test, y_test)
displayBestAlgo(dictAlgoScore, 'R²')
###########################################################################
Classement des différents algorithmes testés selon la métric:--------- R²
Les algorithmes testés sont affichées du plus au moins performant.
##################################################
1 GradientBoostingRegressor------------ R²: 0.76
2 XGBRegressor------------------------- R²: 0.75
3 Ridge-------------------------------- R²: 0.73
4 AdaBoostRegressor-------------------- R²: 0.72
5 Lasso-------------------------------- R²: 0.71
6 SVR---------------------------------- R²: 0.7
7 RandomForestRegressor---------------- R²: 0.7
8 KernelRidge-------------------------- R²: 0.69
9 KNeighborsRegressor------------------ R²: 0.43
10 DummyRegressor----------------------- R²: -0.0
##################################################
Algorithme testé-------------- GradientBoostingRegressor
R²-------------------------------------------0.76
Mean Absolute Error---------------------0.61
Mean Square Error-----------------------0.69
Root Mean Square Error-----------------------0.83
Execution Time-------------------------------254.88
##################################################
Algorithme testé-------------- XGBRegressor
R²-------------------------------------------0.75
Mean Absolute Error---------------------0.63
Mean Square Error-----------------------0.72
Root Mean Square Error-----------------------0.85
Execution Time-------------------------------48.1
##################################################
Algorithme testé-------------- Ridge
R²-------------------------------------------0.73
Mean Absolute Error---------------------0.65
Mean Square Error-----------------------0.78
Root Mean Square Error-----------------------0.88
Execution Time-------------------------------5.4
##################################################
Algorithme testé-------------- AdaBoostRegressor
R²-------------------------------------------0.72
Mean Absolute Error---------------------0.63
Mean Square Error-----------------------0.8
Root Mean Square Error-----------------------0.9
Execution Time-------------------------------279.99
##################################################
Algorithme testé-------------- Lasso
R²-------------------------------------------0.71
Mean Absolute Error---------------------0.67
Mean Square Error-----------------------0.82
Root Mean Square Error-----------------------0.9
Execution Time-------------------------------11.51
##################################################
Algorithme testé-------------- SVR
R²-------------------------------------------0.7
Mean Absolute Error---------------------0.69
Mean Square Error-----------------------0.87
Root Mean Square Error-----------------------0.93
Execution Time-------------------------------13.21
##################################################
Algorithme testé-------------- RandomForestRegressor
R²-------------------------------------------0.7
Mean Absolute Error---------------------0.67
Mean Square Error-----------------------0.84
Root Mean Square Error-----------------------0.92
Execution Time-------------------------------1.41
##################################################
Algorithme testé-------------- KernelRidge
R²-------------------------------------------0.69
Mean Absolute Error---------------------0.72
Mean Square Error-----------------------0.89
Root Mean Square Error-----------------------0.95
Execution Time-------------------------------15.65
##################################################
Algorithme testé-------------- KNeighborsRegressor
R²-------------------------------------------0.43
Mean Absolute Error---------------------0.96
Mean Square Error-----------------------1.62
Root Mean Square Error-----------------------1.27
Execution Time-------------------------------15.56
##################################################
Algorithme testé-------------- DummyRegressor
R²--------------------------------------------0.0
Mean Absolute Error---------------------1.36
Mean Square Error-----------------------2.85
Root Mean Square Error-----------------------1.69
Execution Time-------------------------------0.0
##################################################
###########################################################################
Nous devions réaliser 2 tests distincts sur les bâtiments
de la ville de Seattle (non destinés à l’habitation) afin
d'évaluer notre capacité à prédire :
Nous avons utilisé différents modèles de Machine Learning
pour évaluer notre capacité à prédire les émissions de CO2
et la consommation totale d’énergie.
D’une manière générale, si on se base sur la métrique **R²** :
D’une manière générale, si on se base sur la métrique **RMSE** :
A la vue des différents résultats, sur les deux targets,
j’identifie XGBRegressor comme étant le meilleur algorithme
étant à même de faire de bonnes prédictions à la fois pour
les émissions de gaz à effet de serre et pour la consommation totale
en énergie des bâtiments de Seattle.
Les algorithmes GradientBoostingRegressor et XGBRegressor ont
globalement des scores assez proches si on compare leur R² et leur RMSE.
Même si dans le cas présent, où nos essais ont été réalisés
avec un random_state fixé à 1, GradientBoostingRegressor
s’en sort légèrement mieux que XGBRegressor, ce n’est pas systématiquement le cas.
Globalement, sans random_state particulier, et après de nombreux essais,
j’ai pu observer que **XGBRegressor** s’en sort plus souvent avec
des meilleurs scores que **GradientBoostingRegressor**.
De plus, XGBRegressor à plus de potentiel d’amélioration si on décide
d’aller plus loin dans l’optimisation de ses hyperparamètres même si
cela peut demander beaucoup de temps et de puissance de calcul.
La performance des meilleurs algorithmes a démontré leurs capacités
à prédire efficacement les émissions de CO2 et la consommation totale d’énergie
des bâtiments non destinés à l’habitation de la ville de Seattle.
Pour pouvoir évaluer l'apport de la variable ENERGYSTARScore,
il me faut évaluer mes modèles avec et sans cette variable.
Cependant, il faut également que les tests soient systématiquement
réalisés avec le même jeu de données (le même nombre d'entrées).
Je réalise ici les mêmes tests que précedement mais en filtrant
et en ne conservant uniquement que les enregistrements pour
lesquels **ENERGYSTARScore** est *Non Null*.
Je réaliserai les tests pour chaqu'une des 2 targets,
et en testant à chaque fois avec et sans l'utilisation
de la variable ENERGYSTARScore.
Je remplace le contenu de 'df' par le contenu de 'df_Total',
le DataFrame filtré et optimisé pour la target 'TotalGHGEmissions'.
df = df_Total[df_Total.ENERGYSTARScore.notnull()]
dictAlgoScore = {}
X = df.drop(['TotalGHGEmissions','ENERGYSTARScore'], axis=1)
y = df.TotalGHGEmissions
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=RS)
dummy_regr = DummyRegressor(strategy='mean')
pcStart = perf_counter()
dummy_regr.fit(X,y)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['DummyRegressor'] = displayMetrics(dummy_regr, X_test, y_test, pcStart, pcEnd)
##################################################
R²--------------------------------------------0.02
Mean Absolute Error---------------------1.47
Mean Square Error-----------------------3.35
Root Mean Square Error-----------------------1.83
Process Time---------------------------------0.0s
##################################################
Affichage des prédictions '**y_pred**' en bleu en fonction
de la valeur réelle de y '**y_true**' en rouge :
ShowYTrueYPredError(dummy_regr, X_test, y_test)
J'enregistre également la MSE de ma baseline afin de pouvoir l'afficher
dans des graphiques où on affichera l'erreur des algorithmes en fonction de certains paramètres :
baseline_error = mean_squared_error(y_test, dummy_regr.predict(X_test))
Version Linéaire de l'algorithme des K plus proches voisins.
Je test l'algortithme avec différentes valeurs de k,
allant de k = 1 à k = 49.
Je procède cette fois-ci à une cross-validation.
J'affiche ensuite le score du train-set et du validation-set en fonction de k.
knn = KNeighborsRegressor()
k = np.arange(1, 50)
train_score, val_score = validation_curve(knn,
X_train,
y_train,
'n_neighbors',
k,
cv=5)
plt.plot(k,val_score.mean(axis=1), label='validation')
plt.plot(k, train_score.mean(axis=1), label='train')
plt.ylabel('score')
plt.xlabel('n_neighbors')
plt.legend()
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\utils\validation.py:67: FutureWarning: Pass param_name=n_neighbors, param_range=[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
49] as keyword args. From version 0.25 passing these as positional arguments will result in an error
warnings.warn("Pass {} as keyword args. From version 0.25 "
<matplotlib.legend.Legend at 0x2517252d2e0>
On observe un maximum pour le score du validation-set entre k=15 et k=17.
Je réalise maintenant une recherche des meilleurs *hyperparamètres*
avec **GridSearchCV** en testant les autres *hyperparamètres* existants :
param_grid = {'n_neighbors': np.arange(1,20),
'metric': ['euclidean', 'manhattan'],
'weights': ['uniform', 'distance']}
grid = GridSearchCV(KNeighborsRegressor(),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
knn_opti = grid.best_estimator_
Meilleurs paramètres: {'metric': 'euclidean', 'n_neighbors': 11, 'weights': 'distance'}
Meilleur score: 0.5150691542156777
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KNeighborsRegressor'] = displayMetrics(knn_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.44
Mean Absolute Error---------------------1.02
Mean Square Error-----------------------1.84
Root Mean Square Error-----------------------1.35
Process Time---------------------------------10.58s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(knn_opti, X_test, y_test)
La régression Ridge est un régression linéaire avec une contrainte quadratique sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Ridge va appliquer des pénalités aux variables corrélées
et va appliquer aux variables corrélées le même poids à chacune d'entre-elles.
De cette façon, les variables corrélées n'auront pas plus d'influence
qu'une variable seule non corrélée avec les autres variables du jeu de données.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Ridge avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
ridge = Ridge(random_state=RS)
n_alphas = 1000
alphas = np.logspace(-5,6, n_alphas)
coefs = []
errors = []
for a in alphas:
ridge.set_params(alpha=a)
ridge.fit(X_train, y_train)
coefs.append(ridge.coef_)
errors.append(np.mean((ridge.predict(X_test) - y_test) ** 2))
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**6], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
58
errors[np.argmin(errors)]
0.5953153854692897
alphas[np.argmin(errors)]
4.3514965009250545e-05
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 200)}
grid = GridSearchCV(Ridge(random_state=RS),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
ridge_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 0.09884959046625576}
Meilleur score: 0.7627822082397687
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Ridge'] = displayMetrics(ridge_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.61
Mean Square Error-----------------------0.64
Root Mean Square Error-----------------------0.8
Process Time---------------------------------5.25s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(ridge_opti, X_test, y_test)
La régression Lasso est une régression linéaire avec une contrainte linéaire sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Lasso va appliquer des pénalités aux variables corrélées
et ne conserver qu'une seule variable parmi les variables corrélées
et passer le coefficiant des autres variables corrélées à 0.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Lasso avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
n_alphas = 300
alphas = np.logspace(-5, 1, n_alphas)
lasso = Lasso(fit_intercept=False,
random_state=RS)
coefs = []
errors = []
for a in alphas:
lasso.set_params(alpha=a)
lasso.fit(X_train, y_train)
coefs.append(lasso.coef_)
errors.append(np.mean((lasso.predict(X_test) - y_test) ** 2))
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 330.9543259499117, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 330.9725638210249, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 330.99164177517247, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.0115863810181, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.03243854196114, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.054243365828, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.0770424796751, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.1008732885241, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.1257853592295, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.1518221569368, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.17902904564323, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.2074491941641, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.2371403742126, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.26814621806096, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.3005058113903, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.3342836983315, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.3695140328277, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.40631351331285, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.444781347782, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.48493667116617, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.5268598311887, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.57061816495906, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.616297466555, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.6639820096654, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.7137730227069, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.7657591877463, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.8200670021375, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.8768314875405, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.93611813171907, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.997884313341, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.0624242949652, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.12965497190834, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.19931295417683, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.2713781195848, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.34615689388073, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.42326101421304, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.50354436893684, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.58533766623515, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.6699446365409, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.75742357768235, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.8477938928163, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.94109461873285, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.03729759112304, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.1364156381529, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.2384114113415, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.35414366787853, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.470203594714, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.5896106306163, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.71389627379443, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.8425688604245, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.97547678773043, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 334.11244469197766, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 334.2533128527706, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 334.3978975832064, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 334.54654539339725, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 334.69944595073883, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 334.85587418123765, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 335.0153986927101, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 335.17825261314977, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 335.343991484911, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 335.5124729263235, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 335.68225389262136, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 335.85311959341624, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336.02443812597204, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336.19546203988165, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336.3653755906073, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336.52824455722794, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336.6917465807797, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336.85828845159756, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.0426104655615, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.21787547778314, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.384774074485, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.5420775186078, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.6887502670112, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.8187026595388, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.9033581598491, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.9739957550534, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 338.0061717468623, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.80275568728166, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 337.4376578252255, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336.2911284727397, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 335.26558120854975, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 333.9554486016106, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 332.7572249978136, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 331.6746241541057, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 330.5294479754568, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 329.2130513341108, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 327.6599845923324, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 325.8773019868269, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.7943322865514, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.26421709325683, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 318.2888648451195, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 314.7620373752095, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 310.64110575240034, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 306.0961643953775, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 300.44690078902613, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 293.6320292805589, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 285.1118535742563, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 274.7352578122186, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273.5861616809863, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.71135440941407, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279.0997892930103, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 281.33385448236487, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 283.78752130954575, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 285.9228278043289, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 287.37621289743134, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 283.3002593868755, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279.940497346248, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.6743183678926, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 272.7799254833701, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 269.52033114326184, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 266.24430360856184, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.08737899318874, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 250.96366141711042, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 234.99714658787698, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 207.89879588541837, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202.51502716450716, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 206.46092031596567, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 209.98071938892423, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 213.26917820704725, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 216.33981304019906, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 210.32376708247367, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202.52380176464547, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194.73565551837157, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 186.77401853499228, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 178.39966073236187, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169.59393036148475, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 160.62061822370242, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 150.9393087935553, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 140.78131603355888, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 130.21115803323283, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 119.3193223771828, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 108.09063556229682, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 96.56909048862951, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 84.81678155162876, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 72.88364087628281, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60.917434746991376, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49.09129670299376, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37.49925680421643, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.58174801489315, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.405430010627242, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.508077271464913, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent(
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**1], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
112
errors[np.argmin(errors)]
1.2807843710070286
alphas[np.argmin(errors)]
0.0017680381784572086
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.xlabel('alpha')
plt.ylabel('weights')
plt.axis('tight')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 300)}
grid = GridSearchCV(Lasso(random_state=RS), param_grid, cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
lasso_opti = grid.best_estimator_
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.455948274678917, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.667118340620732, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4150307146747423, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0152687714348474, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.556081307768437, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4299195458339113, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.591853387031051, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4211829182358713, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5223024263649734, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.533239185334452, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4328928640530307, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1468267103941798, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.430691328779318, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.346055761161381, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.549510077983427, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.446645335042092, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0584816275116395, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.442389989688337, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.257265148766777, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.580574620639027, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.437547943947578, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.060872156714879, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.455762380682586, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2469146095550343, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.619351582590525, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8571954100083303, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0735687036493573, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4706778830270935, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2736357963682963, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.303717795032497, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.613588400471059, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0878150215738174, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4871069198239013, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2827194479908997, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.48180650878487, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.566573379231272, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.435668650786596, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5050035243923503, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.298793244229927, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.622242485360886, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5712659910391267, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5257170613716085, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.319982496984693, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.871834046453785, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.588602934293391, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8967192764056335, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3449850006150257, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.010024304271639, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10.904015914965555, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8477695362679185, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.373807179501057, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.116436100040033, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.813480727854852, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9175658684791301, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4057488420091033, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.214430980123637, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.643930563135882, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.928250715052485, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4405419393899592, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.31154618422579, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2259635733357186, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9323466099480697, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4779267787816934, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.410845615450484, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9508926817505028, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.517741594076483, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.45754952231988, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9765964563705722, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.564427174683857, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3511946189335049, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9982887564251257, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6091550013285314, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.20846106323341473, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.018056243296968, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.654599640977466, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0375514431991348, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6984039721063766, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0564283434279105, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9671429863493017, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0625047108623846, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7765209030796427, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent(
Meilleurs paramètres: {'alpha': 0.0025587435201685892}
Meilleur score: 0.7863924278246277
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Lasso'] = displayMetrics(lasso_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.6
Mean Square Error-----------------------0.65
Root Mean Square Error-----------------------0.8
Process Time---------------------------------15.14s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(lasso_opti, X_test, y_test)
Les algorithmes Support Vector Machine sont généralement utilisés dans des problèmes de classification.
L'idée de cet algorithme est de trouver un hyperplan qui divise notre jeu de données en deux.
SVR est la version Regression du Support Vector Machine (SVM).
Cet algorithme fonctionne bien sur de petits dataset.
Son entrainement peut-être long et il est sensible aux Outliers.
Premier essai avec les paramètres par défaut :
svr = SVR()
svr.fit(X_train, y_train)
displayMetrics(svr, X_test, y_test)
##################################################
R²-------------------------------------------0.63
Mean Absolute Error---------------------0.83
Mean Square Error-----------------------1.22
Root Mean Square Error-----------------------1.11
##################################################
{'R²': 0.63, 'MAE': 0.83, 'MSE': 1.22, 'RMSE': 1.11, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **RandomizedSearchCV** :
J'utilise RandomizedSearchCV car la recherche des meilleurs hyperparamètres est particulièrement longue avec SVR
param_grid = {'kernel': ['linear','rbf'],
'degree': range(3,5),
'gamma' : np.logspace(-2, 1, 4),
'C': np.logspace(-3, 3, 7)}
grid = RandomizedSearchCV(SVR(),
param_grid,
cv=5,
scoring='r2',
n_iter = 10)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
svr_opti = grid.best_estimator_
Meilleurs paramètres: {'kernel': 'linear', 'gamma': 0.1, 'degree': 3, 'C': 10.0}
Meilleur score: 0.7680679979999533
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['SVR'] = displayMetrics(svr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.77
Mean Absolute Error---------------------0.64
Mean Square Error-----------------------0.76
Root Mean Square Error-----------------------0.87
Process Time---------------------------------7.11s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(svr_opti, X_test, y_test)
La Regression Ridge à noyau est une regression ridge
à laquelle nous appliquons l'astuce du noyau.
L'astuce du noyau (Kernel Trick) est une méthode qui permet
d'utiliser un classifieur linéaire pour résoudre un problème non linéaire.
L'idée est de transformer l'espace de représentation des données d'entrée
en un espace de plus grande dimension.
La discrimination linéaire dans l'espace de grande dimension
(appelé aussi espace de redescription) est équivalente à une discrimination
non linéaire dans l'espace d'origine.
Premier essai avec les paramètres par défaut :
kr = KernelRidge()
kr.fit(X_train, y_train)
displayMetrics(kr, X_test, y_test)
##################################################
R²-------------------------------------------0.65
Mean Absolute Error---------------------0.81
Mean Square Error-----------------------1.15
Root Mean Square Error-----------------------1.07
##################################################
{'R²': 0.65, 'MAE': 0.81, 'MSE': 1.15, 'RMSE': 1.07, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'kernel':['rbf','linear','poly'],
'degree':[3,4],
'alpha': np.logspace(-2, 2, 5),
'gamma': np.logspace(-2, 1, 4)}
grid = GridSearchCV(KernelRidge(),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
kr_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 0.01, 'degree': 3, 'gamma': 0.01, 'kernel': 'linear'}
Meilleur score: 0.7497344576716879
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KernelRidge'] = displayMetrics(kr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.62
Mean Square Error-----------------------0.65
Root Mean Square Error-----------------------0.81
Process Time---------------------------------8.69s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(kr_opti, X_test, y_test)
L'algorithme Random Forest fait partie des algorithmes qui utilisent
la méthode du bagging et fait donc partie des algorithmes ensemblistes.
Rappel sur le bagging :
Bagging est une contraction de Bootstrap Aggregation.
Le bagging a pour objectif de réduire la variance de l’estimateur (les problèmes liés
au surapprentissage, et donc qu'une petite modification en entrée (jeu de données) entraîne
de grandes différences en sortie).
L'idée est de créer plusieurs entités d'un même modèle
et d'entrainer chacune de ces entités sur une portion aléatoire
de notre jeu de données.
Pour cela on utilise une technique d'échantillonnage appelé Bootstrapping.
Cette technique consiste à replacer après chaque tirage au sort,
les données qui ont été sélectionnées dans notre jeu de données (on parle
alors de tirage avec remise).
De cette manière, on obtient un groupe de modèles diversifiés (ils n'ont
pas tous été nourris avec les mêmes données mais ils partagent cependant
certaines connaissances en commun) ce qui nous permet d’obtenir des majorités
en faveur des bonnes réponses.
On regroupe ensuite les résultats de chaque modèle pour faire notre prédiction finale.
Random Forest est l'algorithme le plus connu utilisant cette technique.
Il utilise comme modèle de base, l'arbre de décision.
Le nom de Random Forest est logiquement choisi comme tel car nous générons
des arbres de décision aléatoirement.
En cela nous créons donc une forêt aléatoire.
Entrainement avec les paramètres par défaut et n_estimator = 1000 :
rfr = RandomForestRegressor(n_estimators=1000,
n_jobs=-1,
random_state=RS)
pcStart = perf_counter()
rfr.fit(X_train, y_train)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['RandomForestRegressor'] = displayMetrics(rfr, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.79
Mean Absolute Error---------------------0.57
Mean Square Error-----------------------0.69
Root Mean Square Error-----------------------0.83
Process Time---------------------------------1.2s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(rfr, X_test, y_test)
AdaBoost est une méthode de Boosting et fait donc
également partie des algorithmes ensemblistes.
L'idée est d'entraîner l'un après l'autre plusieurs modèle relativement faibles
en demandant à chaque modèle d'essayer de corriger les erreurs effectuées par son prédecesseur.
On obtient ainsi un ensemble de modèles complèmentaires dans lequel
les faiblesses des uns sont compensées par les forces des autres.
Ici les modèles sont entraînés en série.
Chaque modèle est en situation d'underfitting mais en les construisant
les uns par dessus les autres, on est capable de réduire le biais général de tous ces modèles.
Adaboost est un algorithme de boosting qui s’appuie sur ce principe,
avec un paramètre de mise à jour adaptatif permettant de donner plus
d’importance aux valeurs difficiles à prédire, donc en boostant les régresseurs
qui réussissent quand d’autres ont échoué.
Entrainement avec les paramètres par défaut :
adb = AdaBoostRegressor(random_state=RS)
adb.fit(X_train, y_train)
displayMetrics(adb, X_test, y_test)
##################################################
R²-------------------------------------------0.71
Mean Absolute Error---------------------0.74
Mean Square Error-----------------------0.95
Root Mean Square Error-----------------------0.97
##################################################
{'R²': 0.71, 'MAE': 0.74, 'MSE': 0.95, 'RMSE': 0.97, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {"base_estimator__max_depth" : np.linspace(10,100,10),
"n_estimators": [100, 200, 300]
}
DTR = DecisionTreeRegressor(random_state=RS)
ABR = AdaBoostRegressor(base_estimator = DTR,
random_state=RS)
grid = GridSearchCV(ABR,
param_grid=param_grid,
scoring = 'r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
adb_opti = grid.best_estimator_
Meilleurs paramètres: {'base_estimator__max_depth': 30.0, 'n_estimators': 300}
Meilleur score: 0.7750861194175135
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['AdaBoostRegressor'] = displayMetrics(adb_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.56
Mean Square Error-----------------------0.66
Root Mean Square Error-----------------------0.81
Process Time---------------------------------171.73s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(adb_opti, X_test, y_test)
Comme son nom l'indique, GradientBoosting est un algorithme de boosting.
En celà il reprend le même principe de fonctionnement qu'AdaBoost.
GradiantBoosting diffère d'AdaBoost sur sa fonction de perte,
qui s'appuie sur la descente de gradient.
La perte représente la pénalité générée lorsque l'estimation par le modèle
n'est pas parfaitement égale à la cible.
Une fonction de perte quantifie cette pénalité sous la forme d'une valeur individuelle.
La descente de gradient est un algorithme qui permet de trouver le minimum d’une fonction.
Le calcul de la dérivée en un point d'une fonction nous permet de définir
la pente de cette fonction et donc de définir le sens dans lequel il faut
se déplacer sur cette fonction pour se rapprocher de son minimum (local).
Entrainement avec les paramètres par défaut :
gbr = GradientBoostingRegressor(random_state=RS)
gbr.fit(X_train, y_train)
displayMetrics(gbr, X_test, y_test)
##################################################
R²-------------------------------------------0.82
Mean Absolute Error---------------------0.59
Mean Square Error-----------------------0.6
Root Mean Square Error-----------------------0.77
##################################################
{'R²': 0.82, 'MAE': 0.59, 'MSE': 0.6, 'RMSE': 0.77, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'learning_rate': [0.1,0.2,0.3],
'n_estimators':[100,200,300,400,500],
'subsample': [0.4,0.5,0.6,1],
'max_depth': [2,3]}
grid = GridSearchCV(GradientBoostingRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
gbr_opti = grid.best_estimator_
Meilleurs paramètres: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 200, 'subsample': 0.5}
Meilleur score: 0.8183525078231064
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['GradientBoostingRegressor'] = displayMetrics(gbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.83
Mean Absolute Error---------------------0.58
Mean Square Error-----------------------0.57
Root Mean Square Error-----------------------0.75
Process Time---------------------------------181.52s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(gbr_opti, X_test, y_test)
XGBoost signifie Extreme Gradient Boosting.
Il est assez similaire à l'algorithme GradientBoosting.
Il s'agit d'une implémentation spécifique du modèle Gradient Boosting
qui utilise des approximations plus précises pour trouver
le meilleur modèle d'arbre de décision.
Les deux algorithmes suivent le principe de descente de gradient.
XGBoost est plus rapide à l'exécution que GradientBoosting.
Liste non exaustive des avantages de XGBoost sur GradientBoosting :
Entrainement avec les paramètres par défaut :
xgbr = XGBRegressor(random_state=RS)
xgbr.fit(X_train, y_train)
displayMetrics(xgbr, X_test, y_test)
##################################################
R²-------------------------------------------0.77
Mean Absolute Error---------------------0.62
Mean Square Error-----------------------0.76
Root Mean Square Error-----------------------0.87
##################################################
{'R²': 0.77, 'MAE': 0.62, 'MSE': 0.76, 'RMSE': 0.87, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'eta':[0.1,0.2],
'gamma':np.linspace(0,0.2,3),
'max_depth': range(2,6,2),
'subsample': np.linspace(0.5,0.8,4),
'colsample_bytree': np.linspace(0.5,0.8,4)
}
grid = GridSearchCV(XGBRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2'
)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
xgbr_opti = grid.best_estimator_
Meilleurs paramètres: {'colsample_bytree': 0.8, 'eta': 0.2, 'gamma': 0.0, 'max_depth': 2, 'subsample': 0.8}
Meilleur score: 0.8108149967281395
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['XGBRegressor'] = displayMetrics(xgbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.6
Mean Square Error-----------------------0.65
Root Mean Square Error-----------------------0.81
Process Time---------------------------------46.81s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(xgbr_opti, X_test, y_test)
displayBestAlgo(dictAlgoScore, 'R²')
###########################################################################
Classement des différents algorithmes testés selon la métric:--------- R²
Les algorithmes testés sont affichées du plus au moins performant.
##################################################
1 GradientBoostingRegressor------------ R²: 0.83
2 Ridge-------------------------------- R²: 0.8
3 Lasso-------------------------------- R²: 0.8
4 KernelRidge-------------------------- R²: 0.8
5 AdaBoostRegressor-------------------- R²: 0.8
6 XGBRegressor------------------------- R²: 0.8
7 RandomForestRegressor---------------- R²: 0.79
8 SVR---------------------------------- R²: 0.77
9 KNeighborsRegressor------------------ R²: 0.44
10 DummyRegressor----------------------- R²: -0.02
##################################################
Algorithme testé-------------- GradientBoostingRegressor
R²-------------------------------------------0.83
Mean Absolute Error---------------------0.58
Mean Square Error-----------------------0.57
Root Mean Square Error-----------------------0.75
Execution Time-------------------------------181.52
##################################################
Algorithme testé-------------- Ridge
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.61
Mean Square Error-----------------------0.64
Root Mean Square Error-----------------------0.8
Execution Time-------------------------------5.25
##################################################
Algorithme testé-------------- Lasso
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.6
Mean Square Error-----------------------0.65
Root Mean Square Error-----------------------0.8
Execution Time-------------------------------15.14
##################################################
Algorithme testé-------------- KernelRidge
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.62
Mean Square Error-----------------------0.65
Root Mean Square Error-----------------------0.81
Execution Time-------------------------------8.69
##################################################
Algorithme testé-------------- AdaBoostRegressor
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.56
Mean Square Error-----------------------0.66
Root Mean Square Error-----------------------0.81
Execution Time-------------------------------171.73
##################################################
Algorithme testé-------------- XGBRegressor
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.6
Mean Square Error-----------------------0.65
Root Mean Square Error-----------------------0.81
Execution Time-------------------------------46.81
##################################################
Algorithme testé-------------- RandomForestRegressor
R²-------------------------------------------0.79
Mean Absolute Error---------------------0.57
Mean Square Error-----------------------0.69
Root Mean Square Error-----------------------0.83
Execution Time-------------------------------1.2
##################################################
Algorithme testé-------------- SVR
R²-------------------------------------------0.77
Mean Absolute Error---------------------0.64
Mean Square Error-----------------------0.76
Root Mean Square Error-----------------------0.87
Execution Time-------------------------------7.11
##################################################
Algorithme testé-------------- KNeighborsRegressor
R²-------------------------------------------0.44
Mean Absolute Error---------------------1.02
Mean Square Error-----------------------1.84
Root Mean Square Error-----------------------1.35
Execution Time-------------------------------10.58
##################################################
Algorithme testé-------------- DummyRegressor
R²--------------------------------------------0.02
Mean Absolute Error---------------------1.47
Mean Square Error-----------------------3.35
Root Mean Square Error-----------------------1.83
Execution Time-------------------------------0.0
##################################################
###########################################################################
df = df_Total[df_Total.ENERGYSTARScore.notnull()]
dictAlgoScore = {}
X = df.drop(['TotalGHGEmissions'], axis=1)
y = df.TotalGHGEmissions
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=RS)
dummy_regr = DummyRegressor(strategy='mean')
pcStart = perf_counter()
dummy_regr.fit(X,y)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['DummyRegressor'] = displayMetrics(dummy_regr, X_test, y_test, pcStart, pcEnd)
##################################################
R²--------------------------------------------0.02
Mean Absolute Error---------------------1.47
Mean Square Error-----------------------3.35
Root Mean Square Error-----------------------1.83
Process Time---------------------------------0.0s
##################################################
Affichage des prédictions '**y_pred**' en bleu en fonction
de la valeur réelle de y '**y_true**' en rouge :
ShowYTrueYPredError(dummy_regr, X_test, y_test)
J'enregistre également la MSE de ma baseline afin de pouvoir l'afficher
dans des graphiques où on affichera l'erreur des algorithmes en fonction de certains paramètres :
baseline_error = mean_squared_error(y_test, dummy_regr.predict(X_test))
Version Linéaire de l'algorithme des K plus proches voisins.
Je test l'algortithme avec différentes valeurs de k,
allant de k = 1 à k = 49.
Je procède cette fois-ci à une cross-validation.
J'affiche ensuite le score du train-set et du validation-set en fonction de k.
knn = KNeighborsRegressor()
k = np.arange(1, 50)
train_score, val_score = validation_curve(knn,
X_train,
y_train,
'n_neighbors',
k,
cv=5)
plt.plot(k,val_score.mean(axis=1), label='validation')
plt.plot(k, train_score.mean(axis=1), label='train')
plt.ylabel('score')
plt.xlabel('n_neighbors')
plt.legend()
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\utils\validation.py:67: FutureWarning: Pass param_name=n_neighbors, param_range=[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
49] as keyword args. From version 0.25 passing these as positional arguments will result in an error
warnings.warn("Pass {} as keyword args. From version 0.25 "
<matplotlib.legend.Legend at 0x25176478d00>
On observe un maximum pour le score du validation-set entre k=15 et k=17.
Je réalise maintenant une recherche des meilleurs *hyperparamètres*
avec **GridSearchCV** en testant les autres *hyperparamètres* existants :
param_grid = {'n_neighbors': np.arange(1,20),
'metric': ['euclidean', 'manhattan'],
'weights': ['uniform', 'distance']}
grid = GridSearchCV(KNeighborsRegressor(),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
knn_opti = grid.best_estimator_
Meilleurs paramètres: {'metric': 'euclidean', 'n_neighbors': 9, 'weights': 'distance'}
Meilleur score: 0.5463273751323244
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KNeighborsRegressor'] = displayMetrics(knn_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.44
Mean Absolute Error---------------------1.0
Mean Square Error-----------------------1.83
Root Mean Square Error-----------------------1.35
Process Time---------------------------------10.75s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(knn_opti, X_test, y_test)
La régression Ridge est un régression linéaire avec une contrainte quadratique sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Ridge va appliquer des pénalités aux variables corrélées
et va appliquer aux variables corrélées le même poids à chacune d'entre-elles.
De cette façon, les variables corrélées n'auront pas plus d'influence
qu'une variable seule non corrélée avec les autres variables du jeu de données.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Ridge avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
ridge = Ridge(random_state=RS)
n_alphas = 1000
alphas = np.logspace(-5,6, n_alphas)
coefs = []
errors = []
for a in alphas:
ridge.set_params(alpha=a)
ridge.fit(X_train, y_train)
coefs.append(ridge.coef_)
errors.append(np.mean((ridge.predict(X_test) - y_test) ** 2))
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**6], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
0
errors[np.argmin(errors)]
0.4719663852693081
alphas[np.argmin(errors)]
1e-05
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 200)}
grid = GridSearchCV(Ridge(random_state=RS),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
ridge_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 1e-05}
Meilleur score: 0.8689782917374334
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Ridge'] = displayMetrics(ridge_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.86
Mean Absolute Error---------------------0.48
Mean Square Error-----------------------0.47
Root Mean Square Error-----------------------0.69
Process Time---------------------------------5.0s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(ridge_opti, X_test, y_test)
La régression Lasso est une régression linéaire avec une contrainte linéaire sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Lasso va appliquer des pénalités aux variables corrélées
et ne conserver qu'une seule variable parmi les variables corrélées
et passer le coefficiant des autres variables corrélées à 0.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Lasso avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
n_alphas = 300
alphas = np.logspace(-5, 1, n_alphas)
lasso = Lasso(fit_intercept=False,
random_state=RS)
coefs = []
errors = []
for a in alphas:
lasso.set_params(alpha=a)
lasso.fit(X_train, y_train)
coefs.append(lasso.coef_)
errors.append(np.mean((lasso.predict(X_test) - y_test) ** 2))
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.0182028091049, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.03938668347115, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.0615436598171, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.0847158221668, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.1089419338354, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.13427128386735, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.1607681587985, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.18846340505974, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.2174250808784, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.2476875971737, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.2793292829439, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.3123913875916, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.3469100369478, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.3830566133904, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.42082960766857, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.46028900818425, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.5014987126432, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.5445440125526, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.5894906769092, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.63643687475445, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.68545254127577, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.73662625376477, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.7900572675366, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.84583589519076, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.90408194732163, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.9649192044449, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.0284578006113, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.09486251512067, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.1643201188741, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.23705265224936, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.31323709720533, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.3928824336203, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.47613197381656, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.5624872345581, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.6519327408501, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.74421593439627, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.8399199287298, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.93865864050014, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.0412953476922, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.1484496095025, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.26069353450333, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.37787720052313, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.50065822861546, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.63528529934314, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.77100745491515, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.9094976425326, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.05407686872707, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.20100263346364, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.35317334012217, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.5105907662844, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.6736677232013, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.8405651764198, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279.0137153686766, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279.19244642319603, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279.3766386792061, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279.56590161695556, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279.7604542107352, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279.96030524869724, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 280.1651078007114, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 280.37324945728255, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 280.5871814365997, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 280.8045897713734, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 281.0254119223712, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 281.2502965405305, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 281.4788277244656, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 281.71007634078444, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 281.94342992456944, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 282.1765881028995, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 282.41178250971575, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 282.64726323948565, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 282.8802276766641, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 283.1096359563658, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 283.33502010962445, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 283.5575152845817, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 283.7767779202726, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 284.01718811435825, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 284.24437828063884, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 284.45803867682946, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 284.6543086777147, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 284.8330814317025, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 284.7400258073451, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 284.6010056906638, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 284.1334012429535, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 283.51583091882253, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 282.7442730974139, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 281.43776207503987, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 280.400518435057, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279.5022337419992, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.5251212932676, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.373719003595, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.00290838314305, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 274.3645022987642, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 272.3891364692028, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 270.04925941072054, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 267.2116329623284, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.95067568463423, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.03540968231005, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255.45336528697067, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 249.8357853721079, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 242.87196856668712, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 234.13682930222046, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 233.9097093489831, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 235.8466597994409, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 238.1510741427362, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 240.13384237953747, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 241.92717977610772, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 243.49541192613987, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 244.6520487226963, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 242.7119755238557, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 240.6273732228065, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 237.77885414205957, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 234.67154553757757, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 231.9696929669177, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 228.26994841121757, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 222.8753921588166, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 214.3307638335822, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200.01593040060027, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 177.50706857846035, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 176.8914239926039, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180.44999111175215, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 183.51874758036243, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 186.52478886910166, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 189.7222509860958, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 184.5336646030799, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 179.0849692640921, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 172.9801008396213, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 167.0420994662972, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 158.69739242757973, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 150.66968340195174, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 143.01580083065284, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 134.99731082679466, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126.60146513725115, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 117.71973958948098, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 108.37625566845935, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98.77779420959746, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 88.86282419563076, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 78.6299934894331, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 68.13386732678288, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.51640877879646, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46.91331851065365, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 36.445516277381785, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.32248399915875, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.69525064495565, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.066347635210604, tolerance: 1.9115360150087006 model = cd_fast.enet_coordinate_descent(
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**1], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
46
errors[np.argmin(errors)]
1.110679662827933
alphas[np.argmin(errors)]
8.376776400682924e-05
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.xlabel('alpha')
plt.ylabel('weights')
plt.axis('tight')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 300)}
grid = GridSearchCV(Lasso(random_state=RS), param_grid, cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
lasso_opti = grid.best_estimator_
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.790782202374672, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.117822352316452, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9773957643522095, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1405949656597159, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.897941173477776, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.820834073484676, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19.123186226610258, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9921561427151033, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.156224257829166, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.922300175786106, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.85148332994838, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.364225378961919, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.682717669765964, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1693085875589304, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.94888882756878, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.883488672612231, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2951512825558211, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.765034631753778, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1822945067969215, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.544747649156733, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.918743157445093, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.273746535790906, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.222151646816727, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1959259550266665, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.175271313738108, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4043174710661503, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2740271568468984, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.363323247798462, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2106148067366291, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.470713924687203, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.308700818881789, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9614138452521388, tolerance: 0.19922309485583573 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5227645743404707, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3603747903599555, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.656851791683209, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7852217976307685, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6008161245942176, tolerance: 0.19885162562085226 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.740425323525258, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.939638810934099, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8693895006076389, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3914396840910115, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.327647776226158, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7109922291275126, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.21876110816214123, tolerance: 0.20141912424874117 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7842633495797173, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5474802343245386, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.21444867953889002, tolerance: 0.20985321725530381 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5577329396355992, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.575374356528485, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5920881466881553, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6123958738439228, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6333039104075482, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6540883502112962, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6742454084431131, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6932292645090854, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7096280063201164, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7227827231165236, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2581729813749689, tolerance: 0.20990902049945048 model = cd_fast.enet_coordinate_descent(
Meilleurs paramètres: {'alpha': 0.001611973357388157}
Meilleur score: 0.8785416817642504
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Lasso'] = displayMetrics(lasso_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.85
Mean Absolute Error---------------------0.48
Mean Square Error-----------------------0.49
Root Mean Square Error-----------------------0.7
Process Time---------------------------------13.77s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(lasso_opti, X_test, y_test)
Les algorithmes Support Vector Machine sont généralement utilisés dans des problèmes de classification.
L'idée de cet algorithme est de trouver un hyperplan qui divise notre jeu de données en deux.
SVR est la version Regression du Support Vector Machine (SVM).
Cet algorithme fonctionne bien sur de petits dataset.
Son entrainement peut-être long et il est sensible aux Outliers.
Premier essai avec les paramètres par défaut :
svr = SVR()
svr.fit(X_train, y_train)
displayMetrics(svr, X_test, y_test)
##################################################
R²-------------------------------------------0.67
Mean Absolute Error---------------------0.78
Mean Square Error-----------------------1.09
Root Mean Square Error-----------------------1.04
##################################################
{'R²': 0.67, 'MAE': 0.78, 'MSE': 1.09, 'RMSE': 1.04, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **RandomizedSearchCV** :
J'utilise RandomizedSearchCV car la recherche des meilleurs hyperparamètres est particulièrement longue avec SVR
param_grid = {'kernel': ['linear','rbf'],
'degree': range(3,5),
'gamma' : np.logspace(-2, 1, 4),
'C': np.logspace(-3, 3, 7)}
grid = RandomizedSearchCV(SVR(),
param_grid,
cv=5,
scoring='r2',
n_iter = 10)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
svr_opti = grid.best_estimator_
Meilleurs paramètres: {'kernel': 'linear', 'gamma': 0.1, 'degree': 3, 'C': 1000.0}
Meilleur score: 0.8771730831514939
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['SVR'] = displayMetrics(svr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.86
Mean Absolute Error---------------------0.47
Mean Square Error-----------------------0.47
Root Mean Square Error-----------------------0.68
Process Time---------------------------------722.93s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(svr_opti, X_test, y_test)
La Regression Ridge à noyau est une regression ridge
à laquelle nous appliquons l'astuce du noyau.
L'astuce du noyau (Kernel Trick) est une méthode qui permet
d'utiliser un classifieur linéaire pour résoudre un problème non linéaire.
L'idée est de transformer l'espace de représentation des données d'entrée
en un espace de plus grande dimension.
La discrimination linéaire dans l'espace de grande dimension
(appelé aussi espace de redescription) est équivalente à une discrimination
non linéaire dans l'espace d'origine.
Premier essai avec les paramètres par défaut :
kr = KernelRidge()
kr.fit(X_train, y_train)
displayMetrics(kr, X_test, y_test)
##################################################
R²-------------------------------------------0.71
Mean Absolute Error---------------------0.74
Mean Square Error-----------------------0.96
Root Mean Square Error-----------------------0.98
##################################################
{'R²': 0.71, 'MAE': 0.74, 'MSE': 0.96, 'RMSE': 0.98, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'kernel':['rbf','linear','poly'],
'degree':[3,4],
'alpha': np.logspace(-2, 2, 5),
'gamma': np.logspace(-2, 1, 4)}
grid = GridSearchCV(KernelRidge(),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
kr_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 0.01, 'degree': 3, 'gamma': 0.01, 'kernel': 'linear'}
Meilleur score: 0.8608653952430337
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KernelRidge'] = displayMetrics(kr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.84
Mean Absolute Error---------------------0.51
Mean Square Error-----------------------0.51
Root Mean Square Error-----------------------0.72
Process Time---------------------------------8.24s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(kr_opti, X_test, y_test)
L'algorithme Random Forest fait partie des algorithmes qui utilisent
la méthode du bagging et fait donc partie des algorithmes ensemblistes.
Rappel sur le bagging :
Bagging est une contraction de Bootstrap Aggregation.
Le bagging a pour objectif de réduire la variance de l’estimateur (les problèmes liés
au surapprentissage, et donc qu'une petite modification en entrée (jeu de données) entraîne
de grandes différences en sortie).
L'idée est de créer plusieurs entités d'un même modèle
et d'entrainer chacune de ces entités sur une portion aléatoire
de notre jeu de données.
Pour cela on utilise une technique d'échantillonnage appelé Bootstrapping.
Cette technique consiste à replacer après chaque tirage au sort,
les données qui ont été sélectionnées dans notre jeu de données (on parle
alors de tirage avec remise).
De cette manière, on obtient un groupe de modèles diversifiés (ils n'ont
pas tous été nourris avec les mêmes données mais ils partagent cependant
certaines connaissances en commun) ce qui nous permet d’obtenir des majorités
en faveur des bonnes réponses.
On regroupe ensuite les résultats de chaque modèle pour faire notre prédiction finale.
Random Forest est l'algorithme le plus connu utilisant cette technique.
Il utilise comme modèle de base, l'arbre de décision.
Le nom de Random Forest est logiquement choisi comme tel car nous générons
des arbres de décision aléatoirement.
En cela nous créons donc une forêt aléatoire.
Entrainement avec les paramètres par défaut et n_estimator = 1000 :
rfr = RandomForestRegressor(n_estimators=1000,
n_jobs=-1,
random_state=RS)
pcStart = perf_counter()
rfr.fit(X_train, y_train)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['RandomForestRegressor'] = displayMetrics(rfr, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.82
Mean Absolute Error---------------------0.5
Mean Square Error-----------------------0.6
Root Mean Square Error-----------------------0.77
Process Time---------------------------------1.2s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(rfr, X_test, y_test)
AdaBoost est une méthode de Boosting et fait donc
également partie des algorithmes ensemblistes.
L'idée est d'entraîner l'un après l'autre plusieurs modèle relativement faibles
en demandant à chaque modèle d'essayer de corriger les erreurs effectuées par son prédecesseur.
On obtient ainsi un ensemble de modèles complèmentaires dans lequel
les faiblesses des uns sont compensées par les forces des autres.
Ici les modèles sont entraînés en série.
Chaque modèle est en situation d'underfitting mais en les construisant
les uns par dessus les autres, on est capable de réduire le biais général de tous ces modèles.
Adaboost est un algorithme de boosting qui s’appuie sur ce principe,
avec un paramètre de mise à jour adaptatif permettant de donner plus
d’importance aux valeurs difficiles à prédire, donc en boostant les régresseurs
qui réussissent quand d’autres ont échoué.
Entrainement avec les paramètres par défaut :
adb = AdaBoostRegressor(random_state=RS)
adb.fit(X_train, y_train)
displayMetrics(adb, X_test, y_test)
##################################################
R²-------------------------------------------0.7
Mean Absolute Error---------------------0.79
Mean Square Error-----------------------0.99
Root Mean Square Error-----------------------1.0
##################################################
{'R²': 0.7, 'MAE': 0.79, 'MSE': 0.99, 'RMSE': 1.0, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {"base_estimator__max_depth" : np.linspace(10,100,10),
"n_estimators": [100, 200, 300]
}
DTR = DecisionTreeRegressor(random_state=RS)
ABR = AdaBoostRegressor(base_estimator = DTR,
random_state=RS)
grid = GridSearchCV(ABR,
param_grid=param_grid,
scoring = 'r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
adb_opti = grid.best_estimator_
Meilleurs paramètres: {'base_estimator__max_depth': 30.0, 'n_estimators': 100}
Meilleur score: 0.8238728742701523
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['AdaBoostRegressor'] = displayMetrics(adb_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.83
Mean Absolute Error---------------------0.49
Mean Square Error-----------------------0.56
Root Mean Square Error-----------------------0.75
Process Time---------------------------------174.64s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(adb_opti, X_test, y_test)
Comme son nom l'indique, GradientBoosting est un algorithme de boosting.
En celà il reprend le même principe de fonctionnement qu'AdaBoost.
GradiantBoosting diffère d'AdaBoost sur sa fonction de perte,
qui s'appuie sur la descente de gradient.
La perte représente la pénalité générée lorsque l'estimation par le modèle
n'est pas parfaitement égale à la cible.
Une fonction de perte quantifie cette pénalité sous la forme d'une valeur individuelle.
La descente de gradient est un algorithme qui permet de trouver le minimum d’une fonction.
Le calcul de la dérivée en un point d'une fonction nous permet de définir
la pente de cette fonction et donc de définir le sens dans lequel il faut
se déplacer sur cette fonction pour se rapprocher de son minimum (local).
Entrainement avec les paramètres par défaut :
gbr = GradientBoostingRegressor(random_state=RS)
gbr.fit(X_train, y_train)
displayMetrics(gbr, X_test, y_test)
##################################################
R²-------------------------------------------0.88
Mean Absolute Error---------------------0.43
Mean Square Error-----------------------0.39
Root Mean Square Error-----------------------0.63
##################################################
{'R²': 0.88, 'MAE': 0.43, 'MSE': 0.39, 'RMSE': 0.63, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'learning_rate': [0.1,0.2,0.3],
'n_estimators':[100,200,300,400,500],
'subsample': [0.4,0.5,0.6,1],
'max_depth': [2,3]}
grid = GridSearchCV(GradientBoostingRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
gbr_opti = grid.best_estimator_
Meilleurs paramètres: {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 500, 'subsample': 1}
Meilleur score: 0.9057599838501769
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['GradientBoostingRegressor'] = displayMetrics(gbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.89
Mean Absolute Error---------------------0.42
Mean Square Error-----------------------0.38
Root Mean Square Error-----------------------0.61
Process Time---------------------------------189.24s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(gbr_opti, X_test, y_test)
XGBoost signifie Extreme Gradient Boosting.
Il est assez similaire à l'algorithme GradientBoosting.
Il s'agit d'une implémentation spécifique du modèle Gradient Boosting
qui utilise des approximations plus précises pour trouver
le meilleur modèle d'arbre de décision.
Les deux algorithmes suivent le principe de descente de gradient.
XGBoost est plus rapide à l'exécution que GradientBoosting.
Liste non exaustive des avantages de XGBoost sur GradientBoosting :
Entrainement avec les paramètres par défaut :
xgbr = XGBRegressor(random_state=RS)
xgbr.fit(X_train, y_train)
displayMetrics(xgbr, X_test, y_test)
##################################################
R²-------------------------------------------0.85
Mean Absolute Error---------------------0.47
Mean Square Error-----------------------0.51
Root Mean Square Error-----------------------0.71
##################################################
{'R²': 0.85, 'MAE': 0.47, 'MSE': 0.51, 'RMSE': 0.71, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'eta':[0.1,0.2],
'gamma':np.linspace(0,0.2,3),
'max_depth': range(2,6,2),
'subsample': np.linspace(0.5,0.8,4),
'colsample_bytree': np.linspace(0.5,0.8,4)
}
grid = GridSearchCV(XGBRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2'
)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
xgbr_opti = grid.best_estimator_
Meilleurs paramètres: {'colsample_bytree': 0.8, 'eta': 0.2, 'gamma': 0.2, 'max_depth': 2, 'subsample': 0.8}
Meilleur score: 0.8928660804441358
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['XGBRegressor'] = displayMetrics(xgbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.86
Mean Absolute Error---------------------0.45
Mean Square Error-----------------------0.45
Root Mean Square Error-----------------------0.67
Process Time---------------------------------48.04s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(xgbr_opti, X_test, y_test)
displayBestAlgo(dictAlgoScore, 'R²')
###########################################################################
Classement des différents algorithmes testés selon la métric:--------- R²
Les algorithmes testés sont affichées du plus au moins performant.
##################################################
1 GradientBoostingRegressor------------ R²: 0.89
2 Ridge-------------------------------- R²: 0.86
3 SVR---------------------------------- R²: 0.86
4 XGBRegressor------------------------- R²: 0.86
5 Lasso-------------------------------- R²: 0.85
6 KernelRidge-------------------------- R²: 0.84
7 AdaBoostRegressor-------------------- R²: 0.83
8 RandomForestRegressor---------------- R²: 0.82
9 KNeighborsRegressor------------------ R²: 0.44
10 DummyRegressor----------------------- R²: -0.02
##################################################
Algorithme testé-------------- GradientBoostingRegressor
R²-------------------------------------------0.89
Mean Absolute Error---------------------0.42
Mean Square Error-----------------------0.38
Root Mean Square Error-----------------------0.61
Execution Time-------------------------------189.24
##################################################
Algorithme testé-------------- Ridge
R²-------------------------------------------0.86
Mean Absolute Error---------------------0.48
Mean Square Error-----------------------0.47
Root Mean Square Error-----------------------0.69
Execution Time-------------------------------5.0
##################################################
Algorithme testé-------------- SVR
R²-------------------------------------------0.86
Mean Absolute Error---------------------0.47
Mean Square Error-----------------------0.47
Root Mean Square Error-----------------------0.68
Execution Time-------------------------------722.93
##################################################
Algorithme testé-------------- XGBRegressor
R²-------------------------------------------0.86
Mean Absolute Error---------------------0.45
Mean Square Error-----------------------0.45
Root Mean Square Error-----------------------0.67
Execution Time-------------------------------48.04
##################################################
Algorithme testé-------------- Lasso
R²-------------------------------------------0.85
Mean Absolute Error---------------------0.48
Mean Square Error-----------------------0.49
Root Mean Square Error-----------------------0.7
Execution Time-------------------------------13.77
##################################################
Algorithme testé-------------- KernelRidge
R²-------------------------------------------0.84
Mean Absolute Error---------------------0.51
Mean Square Error-----------------------0.51
Root Mean Square Error-----------------------0.72
Execution Time-------------------------------8.24
##################################################
Algorithme testé-------------- AdaBoostRegressor
R²-------------------------------------------0.83
Mean Absolute Error---------------------0.49
Mean Square Error-----------------------0.56
Root Mean Square Error-----------------------0.75
Execution Time-------------------------------174.64
##################################################
Algorithme testé-------------- RandomForestRegressor
R²-------------------------------------------0.82
Mean Absolute Error---------------------0.5
Mean Square Error-----------------------0.6
Root Mean Square Error-----------------------0.77
Execution Time-------------------------------1.2
##################################################
Algorithme testé-------------- KNeighborsRegressor
R²-------------------------------------------0.44
Mean Absolute Error---------------------1.0
Mean Square Error-----------------------1.83
Root Mean Square Error-----------------------1.35
Execution Time-------------------------------10.75
##################################################
Algorithme testé-------------- DummyRegressor
R²--------------------------------------------0.02
Mean Absolute Error---------------------1.47
Mean Square Error-----------------------3.35
Root Mean Square Error-----------------------1.83
Execution Time-------------------------------0.0
##################################################
###########################################################################
df = df_Energy[df_Energy.ENERGYSTARScore.notnull()]
dictAlgoScore = {}
X = df.drop(['SiteEnergyUse','ENERGYSTARScore'], axis=1)
y = df.SiteEnergyUse
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=RS)
dummy_regr = DummyRegressor(strategy='mean')
pcStart = perf_counter()
dummy_regr.fit(X,y)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['DummyRegressor'] = displayMetrics(dummy_regr, X_test, y_test, pcStart, pcEnd)
##################################################
R²--------------------------------------------0.01
Mean Absolute Error---------------------1.35
Mean Square Error-----------------------2.75
Root Mean Square Error-----------------------1.66
Process Time---------------------------------0.0s
##################################################
Affichage des prédictions '**y_pred**' en bleu en fonction
de la valeur réelle de y '**y_true**' en rouge :
ShowYTrueYPredError(dummy_regr, X_test, y_test)
J'enregistre également la MSE de ma baseline afin de pouvoir l'afficher
dans des graphiques où on affichera l'erreur des algorithmes en fonction de certains paramètres :
baseline_error = mean_squared_error(y_test, dummy_regr.predict(X_test))
Version Linéaire de l'algorithme des K plus proches voisins.
Je test l'algortithme avec différentes valeurs de k,
allant de k = 1 à k = 49.
Je procède cette fois-ci à une cross-validation.
J'affiche ensuite le score du train-set et du validation-set en fonction de k.
knn = KNeighborsRegressor()
k = np.arange(1, 50)
train_score, val_score = validation_curve(knn,
X_train,
y_train,
'n_neighbors',
k,
cv=5)
plt.plot(k,val_score.mean(axis=1), label='validation')
plt.plot(k, train_score.mean(axis=1), label='train')
plt.ylabel('score')
plt.xlabel('n_neighbors')
plt.legend()
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\utils\validation.py:67: FutureWarning: Pass param_name=n_neighbors, param_range=[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
49] as keyword args. From version 0.25 passing these as positional arguments will result in an error
warnings.warn("Pass {} as keyword args. From version 0.25 "
<matplotlib.legend.Legend at 0x2517648fac0>
On observe un maximum pour le score du validation-set entre k=15 et k=17.
Je réalise maintenant une recherche des meilleurs *hyperparamètres*
avec **GridSearchCV** en testant les autres *hyperparamètres* existants :
param_grid = {'n_neighbors': np.arange(1,20),
'metric': ['euclidean', 'manhattan'],
'weights': ['uniform', 'distance']}
grid = GridSearchCV(KNeighborsRegressor(),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
knn_opti = grid.best_estimator_
Meilleurs paramètres: {'metric': 'manhattan', 'n_neighbors': 15, 'weights': 'distance'}
Meilleur score: 0.4900727618005406
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KNeighborsRegressor'] = displayMetrics(knn_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.42
Mean Absolute Error---------------------0.95
Mean Square Error-----------------------1.57
Root Mean Square Error-----------------------1.25
Process Time---------------------------------9.2s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(knn_opti, X_test, y_test)
La régression Ridge est un régression linéaire avec une contrainte quadratique sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Ridge va appliquer des pénalités aux variables corrélées
et va appliquer aux variables corrélées le même poids à chacune d'entre-elles.
De cette façon, les variables corrélées n'auront pas plus d'influence
qu'une variable seule non corrélée avec les autres variables du jeu de données.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Ridge avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
ridge = Ridge(random_state=RS)
n_alphas = 1000
alphas = np.logspace(-5,6, n_alphas)
coefs = []
errors = []
for a in alphas:
ridge.set_params(alpha=a)
ridge.fit(X_train, y_train)
coefs.append(ridge.coef_)
errors.append(np.mean((ridge.predict(X_test) - y_test) ** 2))
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**6], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
0
errors[np.argmin(errors)]
0.5774735158294684
alphas[np.argmin(errors)]
1e-05
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 200)}
grid = GridSearchCV(Ridge(random_state=RS),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
ridge_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 9.547716114208066e-05}
Meilleur score: 0.7100761991458141
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Ridge'] = displayMetrics(ridge_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.79
Mean Absolute Error---------------------0.58
Mean Square Error-----------------------0.58
Root Mean Square Error-----------------------0.76
Process Time---------------------------------4.93s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(ridge_opti, X_test, y_test)
La régression Lasso est une régression linéaire avec une contrainte linéaire sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Lasso va appliquer des pénalités aux variables corrélées
et ne conserver qu'une seule variable parmi les variables corrélées
et passer le coefficiant des autres variables corrélées à 0.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Lasso avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
n_alphas = 300
alphas = np.logspace(-5, 1, n_alphas)
lasso = Lasso(fit_intercept=False,
random_state=RS)
coefs = []
errors = []
for a in alphas:
lasso.set_params(alpha=a)
lasso.fit(X_train, y_train)
coefs.append(lasso.coef_)
errors.append(np.mean((lasso.predict(X_test) - y_test) ** 2))
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.7755652335859, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.78589253716336, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.796677240172, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.80793858891246, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.8196961368427, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.8319665635592, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.8447827556575, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.8581758960007, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.8721728428247, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.88671800810357, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.90188757696467, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.9177044170889, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.9341926819177, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.9513774609686, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.9692831404612, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.98797180625155, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.00773116010174, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.0283180097019, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.04974688468656, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.07204448079, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.09524023247855, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.11936262426906, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.1444395766803, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.1705007117632, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.19759963873855, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.225722097145, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.25492186151484, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.2852245989687, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.3167025664842, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.34939565010325, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.38337899550896, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.4187626271236, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.4556812236388, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.4931067995316, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.53104279625273, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.5701170718911, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.6103210396379, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.6587545830349, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.7022169337577, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.74936661559605, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.79867373687483, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.8498658152378, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.90284077018384, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.95748547087874, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.0137596160075, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.0716135682163, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.13099757738007, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.19200228077375, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.2545292611313, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.31874993222476, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.3851304456395, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.4549014885441, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.5246442676635, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.59683960648323, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.67447077856326, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.74694933853465, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.8204772406013, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.89488769085295, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.9698752972241, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.04578173938313, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.12271999008857, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.20355593584515, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.28321307779566, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.3570246760836, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.42901850585315, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.49911364899526, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.5664039130071, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.62854332581884, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.6772952226668, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.73624046706647, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.794349267731, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.7752446870365, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.749803915878, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.6864353015674, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.6010046964762, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.4923977896691, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.3529378793668, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323.1757070508117, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.9501596925438, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.69163227360065, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.38302734171907, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 322.01685472253513, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.54587925934305, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 321.028512633644, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 320.42061031459355, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 319.7738153288127, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 318.9300843097608, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 316.23195176602945, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 314.0229011920382, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 311.815927994586, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 309.324078611538, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 306.42846925440597, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 303.01509697278465, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 298.7225650646699, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 293.6990000176708, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 287.69966351493053, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 280.5236529241975, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 271.5883132435595, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 267.838187130651, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 270.6331692164515, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 272.486830758411, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 274.05568291943206, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.7751723699969, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 277.40325131832066, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.54775191834744, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 278.17967671069243, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276.57531689942317, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 275.1647373195644, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273.59888220835103, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 272.7700718272789, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 269.4868447190608, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.8127579603308, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.77943613750847, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 257.4554553623918, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 251.0247853612538, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 240.13467569881328, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 218.1342167256782, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 217.9379169760746, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 222.56799620943752, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 226.52757525556942, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 224.01150325577453, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 218.6939492105035, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 212.69231752798942, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 207.0960143346852, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200.3269704576894, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 188.3316264298987, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180.16853942066632, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171.7743719258116, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 163.0846817512449, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 154.08476336331745, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144.69963833561076, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 135.0446319370102, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 125.18274848359573, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 115.17674896193796, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 105.10969459822974, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 94.77623132749471, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 85.12451897271717, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 75.90854925346423, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 67.45829677251811, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60.24802433415505, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54.704519362551196, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52.58986072088476, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52.90959331963461, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53.220322094313474, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53.58140195364297, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53.94113262831229, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54.31829829335277, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54.64756699810346, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54.819046602239155, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54.996707141079014, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.2567843633517, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.530260614947906, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.806942063418546, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.081419612622994, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.35109909840298, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.64748245489204, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.906550882721945, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.194162903320944, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.3760003443366, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.571853890096804, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.68761823817408, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.76244316265013, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.81461207356392, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.84332851548197, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58.04703741031642, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.99971419049257, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.91730923021146, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.786001713731366, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.58429763121535, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.3156200920663, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.950178832978395, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.522222444016165, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.95552047594538, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.25508693440531, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54.414109969820856, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53.38736745808728, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52.23737128653829, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50.895762683548924, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49.34068835199798, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47.547944339361266, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45.49026976260552, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 43.139563279343975, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40.42590698038157, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37.46622403974675, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34.25747138649683, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30.659179077422095, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.671343000192678, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent(
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**1], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
152
errors[np.argmin(errors)]
1.199200249720102
alphas[np.argmin(errors)]
0.011224505680853071
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.xlabel('alpha')
plt.ylabel('weights')
plt.axis('tight')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 300)}
grid = GridSearchCV(Lasso(random_state=RS), param_grid, cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
lasso_opti = grid.best_estimator_
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65.28896053538647, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10.127871898906108, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.830693022237483, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65.35863893712383, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.289269838644657, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.868035482524022, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34.66730071138829, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.891763412265675, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.903853898803902, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34.63480732400487, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.181386023184416, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.942189652993022, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34.24218093789321, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2528976352025722, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.985216245632756, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34.18464305340569, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2754796405104116, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.031307954085975, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10.417637178780211, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2898079873269523, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.082144936243338, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.900570158955531, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.304212531920598, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.138116489426096, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.374379755608686, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3201060221917942, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.199598466959117, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.596269519761279, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3376654753162711, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.266925200400522, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.43820343528182093, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.356755677286941, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.340353148592698, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3773578148923775, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.42007119465714, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3997830781903815, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.855611557407002, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4234506997794085, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3465235071150232, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7945181367389011, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18124924983931123, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent(
Meilleurs paramètres: {'alpha': 0.0025587435201685892}
Meilleur score: 0.7279484521773794
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Lasso'] = displayMetrics(lasso_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.76
Mean Absolute Error---------------------0.6
Mean Square Error-----------------------0.65
Root Mean Square Error-----------------------0.81
Process Time---------------------------------12.79s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(lasso_opti, X_test, y_test)
Les algorithmes Support Vector Machine sont généralement utilisés dans des problèmes de classification.
L'idée de cet algorithme est de trouver un hyperplan qui divise notre jeu de données en deux.
SVR est la version Regression du Support Vector Machine (SVM).
Cet algorithme fonctionne bien sur de petits dataset.
Son entrainement peut-être long et il est sensible aux Outliers.
Premier essai avec les paramètres par défaut :
svr = SVR()
svr.fit(X_train, y_train)
displayMetrics(svr, X_test, y_test)
##################################################
R²-------------------------------------------0.55
Mean Absolute Error---------------------0.83
Mean Square Error-----------------------1.22
Root Mean Square Error-----------------------1.1
##################################################
{'R²': 0.55, 'MAE': 0.83, 'MSE': 1.22, 'RMSE': 1.1, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **RandomizedSearchCV** :
J'utilise RandomizedSearchCV car la recherche des meilleurs hyperparamètres est particulièrement longue avec SVR
param_grid = {'kernel': ['linear','rbf'],
'degree': range(3,5),
'gamma' : np.logspace(-2, 1, 4),
'C': np.logspace(-3, 3, 7)}
grid = RandomizedSearchCV(SVR(),
param_grid,
cv=5,
scoring='r2',
n_iter = 10)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
svr_opti = grid.best_estimator_
Meilleurs paramètres: {'kernel': 'linear', 'gamma': 0.1, 'degree': 3, 'C': 1000.0}
Meilleur score: 0.7264286216657163
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['SVR'] = displayMetrics(svr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.76
Mean Absolute Error---------------------0.58
Mean Square Error-----------------------0.64
Root Mean Square Error-----------------------0.8
Process Time---------------------------------335.95s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(svr_opti, X_test, y_test)
La Regression Ridge à noyau est une regression ridge
à laquelle nous appliquons l'astuce du noyau.
L'astuce du noyau (Kernel Trick) est une méthode qui permet
d'utiliser un classifieur linéaire pour résoudre un problème non linéaire.
L'idée est de transformer l'espace de représentation des données d'entrée
en un espace de plus grande dimension.
La discrimination linéaire dans l'espace de grande dimension
(appelé aussi espace de redescription) est équivalente à une discrimination
non linéaire dans l'espace d'origine.
Premier essai avec les paramètres par défaut :
kr = KernelRidge()
kr.fit(X_train, y_train)
displayMetrics(kr, X_test, y_test)
##################################################
R²-------------------------------------------0.62
Mean Absolute Error---------------------0.77
Mean Square Error-----------------------1.03
Root Mean Square Error-----------------------1.02
##################################################
{'R²': 0.62, 'MAE': 0.77, 'MSE': 1.03, 'RMSE': 1.02, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'kernel':['rbf','linear','poly'],
'degree':[3,4],
'alpha': np.logspace(-2, 2, 5),
'gamma': np.logspace(-2, 1, 4)}
grid = GridSearchCV(KernelRidge(),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
kr_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 0.01, 'degree': 3, 'gamma': 0.01, 'kernel': 'poly'}
Meilleur score: 0.6747774605790281
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KernelRidge'] = displayMetrics(kr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.73
Mean Absolute Error---------------------0.67
Mean Square Error-----------------------0.73
Root Mean Square Error-----------------------0.86
Process Time---------------------------------8.11s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(kr_opti, X_test, y_test)
L'algorithme Random Forest fait partie des algorithmes qui utilisent
la méthode du bagging et fait donc partie des algorithmes ensemblistes.
Rappel sur le bagging :
Bagging est une contraction de Bootstrap Aggregation.
Le bagging a pour objectif de réduire la variance de l’estimateur (les problèmes liés
au surapprentissage, et donc qu'une petite modification en entrée (jeu de données) entraîne
de grandes différences en sortie).
L'idée est de créer plusieurs entités d'un même modèle
et d'entrainer chacune de ces entités sur une portion aléatoire
de notre jeu de données.
Pour cela on utilise une technique d'échantillonnage appelé Bootstrapping.
Cette technique consiste à replacer après chaque tirage au sort,
les données qui ont été sélectionnées dans notre jeu de données (on parle
alors de tirage avec remise).
De cette manière, on obtient un groupe de modèles diversifiés (ils n'ont
pas tous été nourris avec les mêmes données mais ils partagent cependant
certaines connaissances en commun) ce qui nous permet d’obtenir des majorités
en faveur des bonnes réponses.
On regroupe ensuite les résultats de chaque modèle pour faire notre prédiction finale.
Random Forest est l'algorithme le plus connu utilisant cette technique.
Il utilise comme modèle de base, l'arbre de décision.
Le nom de Random Forest est logiquement choisi comme tel car nous générons
des arbres de décision aléatoirement.
En cela nous créons donc une forêt aléatoire.
Entrainement avec les paramètres par défaut et n_estimator = 1000 :
rfr = RandomForestRegressor(n_estimators=1000,
n_jobs=-1,
random_state=RS)
pcStart = perf_counter()
rfr.fit(X_train, y_train)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['RandomForestRegressor'] = displayMetrics(rfr, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.79
Mean Absolute Error---------------------0.53
Mean Square Error-----------------------0.56
Root Mean Square Error-----------------------0.75
Process Time---------------------------------1.2s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(rfr, X_test, y_test)
AdaBoost est une méthode de Boosting et fait donc
également partie des algorithmes ensemblistes.
L'idée est d'entraîner l'un après l'autre plusieurs modèle relativement faibles
en demandant à chaque modèle d'essayer de corriger les erreurs effectuées par son prédecesseur.
On obtient ainsi un ensemble de modèles complèmentaires dans lequel
les faiblesses des uns sont compensées par les forces des autres.
Ici les modèles sont entraînés en série.
Chaque modèle est en situation d'underfitting mais en les construisant
les uns par dessus les autres, on est capable de réduire le biais général de tous ces modèles.
Adaboost est un algorithme de boosting qui s’appuie sur ce principe,
avec un paramètre de mise à jour adaptatif permettant de donner plus
d’importance aux valeurs difficiles à prédire, donc en boostant les régresseurs
qui réussissent quand d’autres ont échoué.
Entrainement avec les paramètres par défaut :
adb = AdaBoostRegressor(random_state=RS)
adb.fit(X_train, y_train)
displayMetrics(adb, X_test, y_test)
##################################################
R²-------------------------------------------0.71
Mean Absolute Error---------------------0.66
Mean Square Error-----------------------0.79
Root Mean Square Error-----------------------0.89
##################################################
{'R²': 0.71, 'MAE': 0.66, 'MSE': 0.79, 'RMSE': 0.89, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {"base_estimator__max_depth" : np.linspace(10,100,10),
"n_estimators": [100, 200, 300]
}
DTR = DecisionTreeRegressor(random_state=RS)
ABR = AdaBoostRegressor(base_estimator = DTR,
random_state=RS)
grid = GridSearchCV(ABR,
param_grid=param_grid,
scoring = 'r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
adb_opti = grid.best_estimator_
Meilleurs paramètres: {'base_estimator__max_depth': 30.0, 'n_estimators': 200}
Meilleur score: 0.7340977518558078
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['AdaBoostRegressor'] = displayMetrics(adb_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.79
Mean Absolute Error---------------------0.52
Mean Square Error-----------------------0.57
Root Mean Square Error-----------------------0.75
Process Time---------------------------------166.45s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(adb_opti, X_test, y_test)
Comme son nom l'indique, GradientBoosting est un algorithme de boosting.
En celà il reprend le même principe de fonctionnement qu'AdaBoost.
GradiantBoosting diffère d'AdaBoost sur sa fonction de perte,
qui s'appuie sur la descente de gradient.
La perte représente la pénalité générée lorsque l'estimation par le modèle
n'est pas parfaitement égale à la cible.
Une fonction de perte quantifie cette pénalité sous la forme d'une valeur individuelle.
La descente de gradient est un algorithme qui permet de trouver le minimum d’une fonction.
Le calcul de la dérivée en un point d'une fonction nous permet de définir
la pente de cette fonction et donc de définir le sens dans lequel il faut
se déplacer sur cette fonction pour se rapprocher de son minimum (local).
Entrainement avec les paramètres par défaut :
gbr = GradientBoostingRegressor(random_state=RS)
gbr.fit(X_train, y_train)
displayMetrics(gbr, X_test, y_test)
##################################################
R²-------------------------------------------0.81
Mean Absolute Error---------------------0.53
Mean Square Error-----------------------0.52
Root Mean Square Error-----------------------0.72
##################################################
{'R²': 0.81, 'MAE': 0.53, 'MSE': 0.52, 'RMSE': 0.72, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'learning_rate': [0.1,0.2,0.3],
'n_estimators':[100,200,300,400,500],
'subsample': [0.4,0.5,0.6,1],
'max_depth': [2,3]}
grid = GridSearchCV(GradientBoostingRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
gbr_opti = grid.best_estimator_
Meilleurs paramètres: {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 200, 'subsample': 1}
Meilleur score: 0.7594714415850332
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['GradientBoostingRegressor'] = displayMetrics(gbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.82
Mean Absolute Error---------------------0.52
Mean Square Error-----------------------0.5
Root Mean Square Error-----------------------0.71
Process Time---------------------------------173.73s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(gbr_opti, X_test, y_test)
XGBoost signifie Extreme Gradient Boosting.
Il est assez similaire à l'algorithme GradientBoosting.
Il s'agit d'une implémentation spécifique du modèle Gradient Boosting
qui utilise des approximations plus précises pour trouver
le meilleur modèle d'arbre de décision.
Les deux algorithmes suivent le principe de descente de gradient.
XGBoost est plus rapide à l'exécution que GradientBoosting.
Liste non exaustive des avantages de XGBoost sur GradientBoosting :
Entrainement avec les paramètres par défaut :
xgbr = XGBRegressor(random_state=RS)
xgbr.fit(X_train, y_train)
displayMetrics(xgbr, X_test, y_test)
##################################################
R²-------------------------------------------0.79
Mean Absolute Error---------------------0.57
Mean Square Error-----------------------0.56
Root Mean Square Error-----------------------0.75
##################################################
{'R²': 0.79, 'MAE': 0.57, 'MSE': 0.56, 'RMSE': 0.75, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'eta':[0.1,0.2],
'gamma':np.linspace(0,0.2,3),
'max_depth': range(2,6,2),
'subsample': np.linspace(0.5,0.8,4),
'colsample_bytree': np.linspace(0.5,0.8,4)
}
grid = GridSearchCV(XGBRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2'
)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
xgbr_opti = grid.best_estimator_
Meilleurs paramètres: {'colsample_bytree': 0.6, 'eta': 0.1, 'gamma': 0.0, 'max_depth': 4, 'subsample': 0.7000000000000001}
Meilleur score: 0.75911881090972
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['XGBRegressor'] = displayMetrics(xgbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.56
Mean Square Error-----------------------0.54
Root Mean Square Error-----------------------0.73
Process Time---------------------------------42.61s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(xgbr_opti, X_test, y_test)
displayBestAlgo(dictAlgoScore, 'R²')
###########################################################################
Classement des différents algorithmes testés selon la métric:--------- R²
Les algorithmes testés sont affichées du plus au moins performant.
##################################################
1 GradientBoostingRegressor------------ R²: 0.82
2 XGBRegressor------------------------- R²: 0.8
3 Ridge-------------------------------- R²: 0.79
4 RandomForestRegressor---------------- R²: 0.79
5 AdaBoostRegressor-------------------- R²: 0.79
6 Lasso-------------------------------- R²: 0.76
7 SVR---------------------------------- R²: 0.76
8 KernelRidge-------------------------- R²: 0.73
9 KNeighborsRegressor------------------ R²: 0.42
10 DummyRegressor----------------------- R²: -0.01
##################################################
Algorithme testé-------------- GradientBoostingRegressor
R²-------------------------------------------0.82
Mean Absolute Error---------------------0.52
Mean Square Error-----------------------0.5
Root Mean Square Error-----------------------0.71
Execution Time-------------------------------173.73
##################################################
Algorithme testé-------------- XGBRegressor
R²-------------------------------------------0.8
Mean Absolute Error---------------------0.56
Mean Square Error-----------------------0.54
Root Mean Square Error-----------------------0.73
Execution Time-------------------------------42.61
##################################################
Algorithme testé-------------- Ridge
R²-------------------------------------------0.79
Mean Absolute Error---------------------0.58
Mean Square Error-----------------------0.58
Root Mean Square Error-----------------------0.76
Execution Time-------------------------------4.93
##################################################
Algorithme testé-------------- RandomForestRegressor
R²-------------------------------------------0.79
Mean Absolute Error---------------------0.53
Mean Square Error-----------------------0.56
Root Mean Square Error-----------------------0.75
Execution Time-------------------------------1.2
##################################################
Algorithme testé-------------- AdaBoostRegressor
R²-------------------------------------------0.79
Mean Absolute Error---------------------0.52
Mean Square Error-----------------------0.57
Root Mean Square Error-----------------------0.75
Execution Time-------------------------------166.45
##################################################
Algorithme testé-------------- Lasso
R²-------------------------------------------0.76
Mean Absolute Error---------------------0.6
Mean Square Error-----------------------0.65
Root Mean Square Error-----------------------0.81
Execution Time-------------------------------12.79
##################################################
Algorithme testé-------------- SVR
R²-------------------------------------------0.76
Mean Absolute Error---------------------0.58
Mean Square Error-----------------------0.64
Root Mean Square Error-----------------------0.8
Execution Time-------------------------------335.95
##################################################
Algorithme testé-------------- KernelRidge
R²-------------------------------------------0.73
Mean Absolute Error---------------------0.67
Mean Square Error-----------------------0.73
Root Mean Square Error-----------------------0.86
Execution Time-------------------------------8.11
##################################################
Algorithme testé-------------- KNeighborsRegressor
R²-------------------------------------------0.42
Mean Absolute Error---------------------0.95
Mean Square Error-----------------------1.57
Root Mean Square Error-----------------------1.25
Execution Time-------------------------------9.2
##################################################
Algorithme testé-------------- DummyRegressor
R²--------------------------------------------0.01
Mean Absolute Error---------------------1.35
Mean Square Error-----------------------2.75
Root Mean Square Error-----------------------1.66
Execution Time-------------------------------0.0
##################################################
###########################################################################
df = df_Energy[df_Energy.ENERGYSTARScore.notnull()]
dictAlgoScore = {}
X = df.drop(['SiteEnergyUse'], axis=1)
y = df.SiteEnergyUse
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=RS)
dummy_regr = DummyRegressor(strategy='mean')
pcStart = perf_counter()
dummy_regr.fit(X,y)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['DummyRegressor'] = displayMetrics(dummy_regr, X_test, y_test, pcStart, pcEnd)
##################################################
R²--------------------------------------------0.01
Mean Absolute Error---------------------1.35
Mean Square Error-----------------------2.75
Root Mean Square Error-----------------------1.66
Process Time---------------------------------0.0s
##################################################
Affichage des prédictions '**y_pred**' en bleu en fonction
de la valeur réelle de y '**y_true**' en rouge :
ShowYTrueYPredError(dummy_regr, X_test, y_test)
J'enregistre également la MSE de ma baseline afin de pouvoir l'afficher
dans des graphiques où on affichera l'erreur des algorithmes en fonction de certains paramètres :
baseline_error = mean_squared_error(y_test, dummy_regr.predict(X_test))
Version Linéaire de l'algorithme des K plus proches voisins.
Je test l'algortithme avec différentes valeurs de k,
allant de k = 1 à k = 49.
Je procède cette fois-ci à une cross-validation.
J'affiche ensuite le score du train-set et du validation-set en fonction de k.
knn = KNeighborsRegressor()
k = np.arange(1, 50)
train_score, val_score = validation_curve(knn,
X_train,
y_train,
'n_neighbors',
k,
cv=5)
plt.plot(k,val_score.mean(axis=1), label='validation')
plt.plot(k, train_score.mean(axis=1), label='train')
plt.ylabel('score')
plt.xlabel('n_neighbors')
plt.legend()
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\utils\validation.py:67: FutureWarning: Pass param_name=n_neighbors, param_range=[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
49] as keyword args. From version 0.25 passing these as positional arguments will result in an error
warnings.warn("Pass {} as keyword args. From version 0.25 "
<matplotlib.legend.Legend at 0x25174bdca60>
On observe un maximum pour le score du validation-set entre k=15 et k=17.
Je réalise maintenant une recherche des meilleurs *hyperparamètres*
avec **GridSearchCV** en testant les autres *hyperparamètres* existants :
param_grid = {'n_neighbors': np.arange(1,20),
'metric': ['euclidean', 'manhattan'],
'weights': ['uniform', 'distance']}
grid = GridSearchCV(KNeighborsRegressor(),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
knn_opti = grid.best_estimator_
Meilleurs paramètres: {'metric': 'manhattan', 'n_neighbors': 11, 'weights': 'distance'}
Meilleur score: 0.5289524856304872
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KNeighborsRegressor'] = displayMetrics(knn_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.42
Mean Absolute Error---------------------0.94
Mean Square Error-----------------------1.59
Root Mean Square Error-----------------------1.26
Process Time---------------------------------9.43s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(knn_opti, X_test, y_test)
La régression Ridge est un régression linéaire avec une contrainte quadratique sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Ridge va appliquer des pénalités aux variables corrélées
et va appliquer aux variables corrélées le même poids à chacune d'entre-elles.
De cette façon, les variables corrélées n'auront pas plus d'influence
qu'une variable seule non corrélée avec les autres variables du jeu de données.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Ridge avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
ridge = Ridge(random_state=RS)
n_alphas = 1000
alphas = np.logspace(-5,6, n_alphas)
coefs = []
errors = []
for a in alphas:
ridge.set_params(alpha=a)
ridge.fit(X_train, y_train)
coefs.append(ridge.coef_)
errors.append(np.mean((ridge.predict(X_test) - y_test) ** 2))
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**6], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
0
errors[np.argmin(errors)]
0.41057373594761676
alphas[np.argmin(errors)]
1e-05
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 200)}
grid = GridSearchCV(Ridge(random_state=RS),
param_grid,
cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
ridge_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 1e-05}
Meilleur score: 0.842322453142992
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Ridge'] = displayMetrics(ridge_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.85
Mean Absolute Error---------------------0.44
Mean Square Error-----------------------0.41
Root Mean Square Error-----------------------0.64
Process Time---------------------------------5.05s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(ridge_opti, X_test, y_test)
La régression Lasso est une régression linéaire avec une contrainte linéaire sur les coefficients.
Elle est utile lorsque les variables sont très corrélées, ce qui fausse souvent la résolution numérique.
La Régression Lasso va appliquer des pénalités aux variables corrélées
et ne conserver qu'une seule variable parmi les variables corrélées
et passer le coefficiant des autres variables corrélées à 0.
La pénalité appliquée aux variables dépend d'un paramètre alpha.
Je vais donc tester la Régression Lasso avec plusieurs valeurs possibles d'alpha.
Je réalise une première application 'à la main' où j'afficherai les pénalités
appliquées aux variables en fonction de alpha, puis j'effectuerai dans un second temps
une recherche du meilleur alpha ainsi qu'une CrossValidation avec GridSearchCV.
n_alphas = 300
alphas = np.logspace(-5, 1, n_alphas)
lasso = Lasso(fit_intercept=False,
random_state=RS)
coefs = []
errors = []
for a in alphas:
lasso.set_params(alpha=a)
lasso.fit(X_train, y_train)
coefs.append(lasso.coef_)
errors.append(np.mean((lasso.predict(X_test) - y_test) ** 2))
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259.9305637025103, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259.9446901182932, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259.9594517627556, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259.97488762849366, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259.99102148303825, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.0078833998082, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.0255025765006, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.0439127505086, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.0631466291772, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.08323971960203, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.10422606174103, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.1261427655238, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.1490283947006, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.17292142287295, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.19786199039663, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.22389363884855, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.2510581612314, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.27938578466876, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.3089475041152, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.3397748650341, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.371920401258, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.4054289917407, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.440355407677, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.47682477662795, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.514809764244, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.5543398670512, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.59545487817564, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.6383490987299, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.6833908942263, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.73024939311995, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.7813603830168, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.83331932035253, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.88828775048887, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.9464619365623, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.00637336916543, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.0676554825481, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.13119624436365, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.1972356135261, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.26594223985967, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.337427311107, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.41175907070686, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.48913907768815, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.56963119145286, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.6532829745366, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.74034890793877, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.8308631058644, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.9247954313797, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.02234954711724, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.1235193040791, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.228412012161, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.3360894168975, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.4486089778865, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.5660302362727, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.687903565074, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.81437436146615, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.9452786352062, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.08053152453675, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.2206147444082, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.3654904585018, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.51365005443773, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.6558813956754, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.80092099807865, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.9486295878884, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.09837876591325, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.2531168000566, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.40722653254255, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.55894055575516, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.7131857457459, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.867160167088, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.0200702410758, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.1737161497105, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.2196692124011, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.2727519764017, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.33675787256726, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.39583562410047, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.4422520782058, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.47047894733595, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.4744733987007, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.45053671342515, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.42089510969913, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.3278819506067, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 265.1801199632274, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.98466170412337, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.72741347028904, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.5002180084564, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 264.1968095458289, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.8167145358578, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 263.2137858042913, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262.5592476337403, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261.7887879181758, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 260.8692081266405, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 259.7726423018036, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258.468925819031, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 256.8975891390533, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 254.91172245228535, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252.97210779818596, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 250.76249918415297, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 248.12731352345423, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 245.0474962552617, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 241.45681381918928, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 237.1189611296249, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 231.97341568838542, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 225.77270930244447, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 218.49527600554518, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 218.56231998826604, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220.19357800788873, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220.82200626300119, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220.93510362690907, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220.39926200113868, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220.03694356450754, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 219.75115664215573, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 219.5761270121093, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 219.46074545282036, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 219.28883732030422, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 218.31570382426298, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 217.24540418441882, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 217.21796865473038, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 214.9056496425614, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200.16997964550836, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191.60959567008751, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 195.34207684408025, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198.84297168323306, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 195.72760258737725, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191.47048364582258, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 186.9325658412306, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 182.15662962878525, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 177.1137868556473, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171.8324833009708, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166.30500359822346, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 160.8566248886075, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 152.44230213806458, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 141.62969482729812, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 133.81601937647707, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126.09485364269733, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 118.14166893814041, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 109.99012306721141, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 101.70364142219591, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 93.37254495856394, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 85.12477396254263, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 76.99721695438183, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 69.64074924580979, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63.08151386410475, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.68000102802489, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54.806378108583885, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.08410950953157, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.44367895653704, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.67794308593011, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.93518899818855, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.18590526285402, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.435792548310815, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.687858492713985, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.936182567852256, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.048699874042995, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.17644943480764, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.28218113146147, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.40575096693283, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.51101126661035, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.607712382978434, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.706006785346176, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.754179549663945, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.77509035066328, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.76148747241541, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.713312587701125, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.629671502699125, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.82757831215463, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.67659663039694, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.47467148075668, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57.164305623240125, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.75859489098832, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56.27859361770106, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.68617513194897, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.01413170242154, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54.223779913750036, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53.29631619534166, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52.22282962328279, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50.921636146229275, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49.46734495594217, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47.86499383973546, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46.071124217997294, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44.057122327686216, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 41.77959582118376, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39.25987901302278, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 36.52622555981134, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 33.45045920294581, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 29.995573094802467, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.121370016806168, tolerance: 24.130455952597625 model = cd_fast.enet_coordinate_descent(
ax = plt.gca()
ax.plot(alphas, errors, [10**-5, 10**1], [baseline_error, baseline_error])
ax.set_xscale('log')
plt.show()
np.argmin(errors)
156
errors[np.argmin(errors)]
1.0020203059276187
alphas[np.argmin(errors)]
0.013503140378698736
plt.figure(figsize=(10,10))
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.xlabel('alpha')
plt.ylabel('weights')
plt.axis('tight')
plt.show()
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'alpha': np.logspace(-5, 10, 300)}
grid = GridSearchCV(Lasso(random_state=RS), param_grid, cv=5)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
lasso_opti = grid.best_estimator_
C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 32.926752279304935, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.580167346425114, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.374247312591663, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.1962060820639095, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.441624445505525, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18.948302785016068, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.604357609746174, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.399220200106541, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.22173933348769, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.465174059961726, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.355172965969643, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.631249974158891, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.426746562691193, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.250271733929907, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.491021941798422, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.306650515597354, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.661090300710171, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.457063354658729, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.283203795670417, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.519499637471384, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.301312281242204, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.6941552119928645, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.490391158040936, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.328363703496919, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.551000862981184, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.339320735138685, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.73084552967758, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.52694757550357, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.46815939574897, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.58569061250661, tolerance: 0.15208229338138207 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.395905982780747, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.771783418892923, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.566957101041254, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.373510747442552, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.46230255155423, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.817245451047171, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.610586234164913, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.41934490633534, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.535456007174744, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.86766546496861, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.657956329416066, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.46941639137529, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.615228141125982, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.920612029253618, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.709140588323635, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.523879555140823, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.701402706553935, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.974292169367018, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.764139487039742, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.582786239258041, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.79383841159438, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6203995975366183, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.822749421174024, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.646155143575925, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.892666025453337, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.21581839808760606, tolerance: 0.1558135454755951 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0667235778947202, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2968497048289152, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.224172521039563, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7797683257357733, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3291228920116964, tolerance: 0.1559707934773599 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2667859406923867, tolerance: 0.16126294909239577 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8727717215108299, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9070556710026239, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9312141358898316, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent( C:\Users\waldu\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.32604792306315744, tolerance: 0.15662657272600905 model = cd_fast.enet_coordinate_descent(
Meilleurs paramètres: {'alpha': 0.0018093604107387166}
Meilleur score: 0.852155198444426
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['Lasso'] = displayMetrics(lasso_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.83
Mean Absolute Error---------------------0.46
Mean Square Error-----------------------0.47
Root Mean Square Error-----------------------0.68
Process Time---------------------------------13.15s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(lasso_opti, X_test, y_test)
Les algorithmes Support Vector Machine sont généralement utilisés dans des problèmes de classification.
L'idée de cet algorithme est de trouver un hyperplan qui divise notre jeu de données en deux.
SVR est la version Regression du Support Vector Machine (SVM).
Cet algorithme fonctionne bien sur de petits dataset.
Son entrainement peut-être long et il est sensible aux Outliers.
Premier essai avec les paramètres par défaut :
svr = SVR()
svr.fit(X_train, y_train)
displayMetrics(svr, X_test, y_test)
##################################################
R²-------------------------------------------0.6
Mean Absolute Error---------------------0.78
Mean Square Error-----------------------1.08
Root Mean Square Error-----------------------1.04
##################################################
{'R²': 0.6, 'MAE': 0.78, 'MSE': 1.08, 'RMSE': 1.04, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **RandomizedSearchCV** :
J'utilise RandomizedSearchCV car la recherche des meilleurs hyperparamètres est particulièrement longue avec SVR
param_grid = {'kernel': ['linear','rbf'],
'degree': range(3,5),
'gamma' : np.logspace(-2, 1, 4),
'C': np.logspace(-3, 3, 7)}
grid = RandomizedSearchCV(SVR(),
param_grid,
cv=5,
scoring='r2',
n_iter = 10)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
svr_opti = grid.best_estimator_
Meilleurs paramètres: {'kernel': 'linear', 'gamma': 1.0, 'degree': 4, 'C': 100.0}
Meilleur score: 0.8472478304891304
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['SVR'] = displayMetrics(svr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.84
Mean Absolute Error---------------------0.43
Mean Square Error-----------------------0.42
Root Mean Square Error-----------------------0.65
Process Time---------------------------------54.85s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(svr_opti, X_test, y_test)
La Regression Ridge à noyau est une regression ridge
à laquelle nous appliquons l'astuce du noyau.
L'astuce du noyau (Kernel Trick) est une méthode qui permet
d'utiliser un classifieur linéaire pour résoudre un problème non linéaire.
L'idée est de transformer l'espace de représentation des données d'entrée
en un espace de plus grande dimension.
La discrimination linéaire dans l'espace de grande dimension
(appelé aussi espace de redescription) est équivalente à une discrimination
non linéaire dans l'espace d'origine.
Premier essai avec les paramètres par défaut :
kr = KernelRidge()
kr.fit(X_train, y_train)
displayMetrics(kr, X_test, y_test)
##################################################
R²-------------------------------------------0.7
Mean Absolute Error---------------------0.69
Mean Square Error-----------------------0.82
Root Mean Square Error-----------------------0.91
##################################################
{'R²': 0.7, 'MAE': 0.69, 'MSE': 0.82, 'RMSE': 0.91, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'kernel':['rbf','linear','poly'],
'degree':[3,4],
'alpha': np.logspace(-2, 2, 5),
'gamma': np.logspace(-2, 1, 4)}
grid = GridSearchCV(KernelRidge(),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
kr_opti = grid.best_estimator_
Meilleurs paramètres: {'alpha': 0.01, 'degree': 3, 'gamma': 0.01, 'kernel': 'poly'}
Meilleur score: 0.8102527617997556
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['KernelRidge'] = displayMetrics(kr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.82
Mean Absolute Error---------------------0.52
Mean Square Error-----------------------0.5
Root Mean Square Error-----------------------0.71
Process Time---------------------------------8.21s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(kr_opti, X_test, y_test)
L'algorithme Random Forest fait partie des algorithmes qui utilisent
la méthode du bagging et fait donc partie des algorithmes ensemblistes.
Rappel sur le bagging :
Bagging est une contraction de Bootstrap Aggregation.
Le bagging a pour objectif de réduire la variance de l’estimateur (les problèmes liés
au surapprentissage, et donc qu'une petite modification en entrée (jeu de données) entraîne
de grandes différences en sortie).
L'idée est de créer plusieurs entités d'un même modèle
et d'entrainer chacune de ces entités sur une portion aléatoire
de notre jeu de données.
Pour cela on utilise une technique d'échantillonnage appelé Bootstrapping.
Cette technique consiste à replacer après chaque tirage au sort,
les données qui ont été sélectionnées dans notre jeu de données (on parle
alors de tirage avec remise).
De cette manière, on obtient un groupe de modèles diversifiés (ils n'ont
pas tous été nourris avec les mêmes données mais ils partagent cependant
certaines connaissances en commun) ce qui nous permet d’obtenir des majorités
en faveur des bonnes réponses.
On regroupe ensuite les résultats de chaque modèle pour faire notre prédiction finale.
Random Forest est l'algorithme le plus connu utilisant cette technique.
Il utilise comme modèle de base, l'arbre de décision.
Le nom de Random Forest est logiquement choisi comme tel car nous générons
des arbres de décision aléatoirement.
En cela nous créons donc une forêt aléatoire.
Entrainement avec les paramètres par défaut et n_estimator = 1000 :
rfr = RandomForestRegressor(n_estimators=1000,
n_jobs=-1,
random_state=RS)
pcStart = perf_counter()
rfr.fit(X_train, y_train)
pcEnd = perf_counter()
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['RandomForestRegressor'] = displayMetrics(rfr, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.84
Mean Absolute Error---------------------0.42
Mean Square Error-----------------------0.45
Root Mean Square Error-----------------------0.67
Process Time---------------------------------1.21s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(rfr, X_test, y_test)
AdaBoost est une méthode de Boosting et fait donc
également partie des algorithmes ensemblistes.
L'idée est d'entraîner l'un après l'autre plusieurs modèle relativement faibles
en demandant à chaque modèle d'essayer de corriger les erreurs effectuées par son prédecesseur.
On obtient ainsi un ensemble de modèles complèmentaires dans lequel
les faiblesses des uns sont compensées par les forces des autres.
Ici les modèles sont entraînés en série.
Chaque modèle est en situation d'underfitting mais en les construisant
les uns par dessus les autres, on est capable de réduire le biais général de tous ces modèles.
Adaboost est un algorithme de boosting qui s’appuie sur ce principe,
avec un paramètre de mise à jour adaptatif permettant de donner plus
d’importance aux valeurs difficiles à prédire, donc en boostant les régresseurs
qui réussissent quand d’autres ont échoué.
Entrainement avec les paramètres par défaut :
adb = AdaBoostRegressor(random_state=RS)
adb.fit(X_train, y_train)
displayMetrics(adb, X_test, y_test)
##################################################
R²-------------------------------------------0.77
Mean Absolute Error---------------------0.57
Mean Square Error-----------------------0.63
Root Mean Square Error-----------------------0.79
##################################################
{'R²': 0.77, 'MAE': 0.57, 'MSE': 0.63, 'RMSE': 0.79, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {"base_estimator__max_depth" : np.linspace(10,100,10),
"n_estimators": [100, 200, 300]
}
DTR = DecisionTreeRegressor(random_state=RS)
ABR = AdaBoostRegressor(base_estimator = DTR,
random_state=RS)
grid = GridSearchCV(ABR,
param_grid=param_grid,
scoring = 'r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
adb_opti = grid.best_estimator_
Meilleurs paramètres: {'base_estimator__max_depth': 30.0, 'n_estimators': 200}
Meilleur score: 0.8322618250286726
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['AdaBoostRegressor'] = displayMetrics(adb_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.84
Mean Absolute Error---------------------0.42
Mean Square Error-----------------------0.45
Root Mean Square Error-----------------------0.67
Process Time---------------------------------167.98s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(adb_opti, X_test, y_test)
Comme son nom l'indique, GradientBoosting est un algorithme de boosting.
En celà il reprend le même principe de fonctionnement qu'AdaBoost.
GradiantBoosting diffère d'AdaBoost sur sa fonction de perte,
qui s'appuie sur la descente de gradient.
La perte représente la pénalité générée lorsque l'estimation par le modèle
n'est pas parfaitement égale à la cible.
Une fonction de perte quantifie cette pénalité sous la forme d'une valeur individuelle.
La descente de gradient est un algorithme qui permet de trouver le minimum d’une fonction.
Le calcul de la dérivée en un point d'une fonction nous permet de définir
la pente de cette fonction et donc de définir le sens dans lequel il faut
se déplacer sur cette fonction pour se rapprocher de son minimum (local).
Entrainement avec les paramètres par défaut :
gbr = GradientBoostingRegressor(random_state=RS)
gbr.fit(X_train, y_train)
displayMetrics(gbr, X_test, y_test)
##################################################
R²-------------------------------------------0.86
Mean Absolute Error---------------------0.41
Mean Square Error-----------------------0.37
Root Mean Square Error-----------------------0.61
##################################################
{'R²': 0.86, 'MAE': 0.41, 'MSE': 0.37, 'RMSE': 0.61, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'learning_rate': [0.1,0.2,0.3],
'n_estimators':[100,200,300,400,500],
'subsample': [0.4,0.5,0.6,1],
'max_depth': [2,3]}
grid = GridSearchCV(GradientBoostingRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2')
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
gbr_opti = grid.best_estimator_
Meilleurs paramètres: {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 400, 'subsample': 1}
Meilleur score: 0.8816514469003026
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['GradientBoostingRegressor'] = displayMetrics(gbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.88
Mean Absolute Error---------------------0.38
Mean Square Error-----------------------0.32
Root Mean Square Error-----------------------0.57
Process Time---------------------------------180.91s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(gbr_opti, X_test, y_test)
XGBoost signifie Extreme Gradient Boosting.
Il est assez similaire à l'algorithme GradientBoosting.
Il s'agit d'une implémentation spécifique du modèle Gradient Boosting
qui utilise des approximations plus précises pour trouver
le meilleur modèle d'arbre de décision.
Les deux algorithmes suivent le principe de descente de gradient.
XGBoost est plus rapide à l'exécution que GradientBoosting.
Liste non exaustive des avantages de XGBoost sur GradientBoosting :
Entrainement avec les paramètres par défaut :
xgbr = XGBRegressor(random_state=RS)
xgbr.fit(X_train, y_train)
displayMetrics(xgbr, X_test, y_test)
##################################################
R²-------------------------------------------0.85
Mean Absolute Error---------------------0.45
Mean Square Error-----------------------0.41
Root Mean Square Error-----------------------0.64
##################################################
{'R²': 0.85, 'MAE': 0.45, 'MSE': 0.41, 'RMSE': 0.64, 'EXECUTION-TIME': 0}
Optimisation des *hyperparamètres* avec **GridSearchCV** :
param_grid = {'eta':[0.1,0.2],
'gamma':np.linspace(0,0.2,3),
'max_depth': range(2,6,2),
'subsample': np.linspace(0.5,0.8,4),
'colsample_bytree': np.linspace(0.5,0.8,4)
}
grid = GridSearchCV(XGBRegressor(random_state=RS),
param_grid,
cv=5,
scoring='r2'
)
pcStart = perf_counter()
grid.fit(X_train, y_train)
pcEnd = perf_counter()
print('Meilleurs paramètres:',grid.best_params_)
print('Meilleur score:', grid.best_score_)
xgbr_opti = grid.best_estimator_
Meilleurs paramètres: {'colsample_bytree': 0.8, 'eta': 0.2, 'gamma': 0.0, 'max_depth': 4, 'subsample': 0.8}
Meilleur score: 0.877652512963147
J'enregistre le score de réference dans le dictionnaire *dictAlgoScore*
et j'affiche les résultats des différentes métriques :
dictAlgoScore['XGBRegressor'] = displayMetrics(xgbr_opti, X_test, y_test, pcStart, pcEnd)
##################################################
R²-------------------------------------------0.87
Mean Absolute Error---------------------0.39
Mean Square Error-----------------------0.35
Root Mean Square Error-----------------------0.59
Process Time---------------------------------45.11s
##################################################
Affichage de y_true en fonction de y_pred :
ShowYTrueYPredError(xgbr_opti, X_test, y_test)
displayBestAlgo(dictAlgoScore, 'R²')
###########################################################################
Classement des différents algorithmes testés selon la métric:--------- R²
Les algorithmes testés sont affichées du plus au moins performant.
##################################################
1 GradientBoostingRegressor------------ R²: 0.88
2 XGBRegressor------------------------- R²: 0.87
3 Ridge-------------------------------- R²: 0.85
4 SVR---------------------------------- R²: 0.84
5 RandomForestRegressor---------------- R²: 0.84
6 AdaBoostRegressor-------------------- R²: 0.84
7 Lasso-------------------------------- R²: 0.83
8 KernelRidge-------------------------- R²: 0.82
9 KNeighborsRegressor------------------ R²: 0.42
10 DummyRegressor----------------------- R²: -0.01
##################################################
Algorithme testé-------------- GradientBoostingRegressor
R²-------------------------------------------0.88
Mean Absolute Error---------------------0.38
Mean Square Error-----------------------0.32
Root Mean Square Error-----------------------0.57
Execution Time-------------------------------180.91
##################################################
Algorithme testé-------------- XGBRegressor
R²-------------------------------------------0.87
Mean Absolute Error---------------------0.39
Mean Square Error-----------------------0.35
Root Mean Square Error-----------------------0.59
Execution Time-------------------------------45.11
##################################################
Algorithme testé-------------- Ridge
R²-------------------------------------------0.85
Mean Absolute Error---------------------0.44
Mean Square Error-----------------------0.41
Root Mean Square Error-----------------------0.64
Execution Time-------------------------------5.05
##################################################
Algorithme testé-------------- SVR
R²-------------------------------------------0.84
Mean Absolute Error---------------------0.43
Mean Square Error-----------------------0.42
Root Mean Square Error-----------------------0.65
Execution Time-------------------------------54.85
##################################################
Algorithme testé-------------- RandomForestRegressor
R²-------------------------------------------0.84
Mean Absolute Error---------------------0.42
Mean Square Error-----------------------0.45
Root Mean Square Error-----------------------0.67
Execution Time-------------------------------1.21
##################################################
Algorithme testé-------------- AdaBoostRegressor
R²-------------------------------------------0.84
Mean Absolute Error---------------------0.42
Mean Square Error-----------------------0.45
Root Mean Square Error-----------------------0.67
Execution Time-------------------------------167.98
##################################################
Algorithme testé-------------- Lasso
R²-------------------------------------------0.83
Mean Absolute Error---------------------0.46
Mean Square Error-----------------------0.47
Root Mean Square Error-----------------------0.68
Execution Time-------------------------------13.15
##################################################
Algorithme testé-------------- KernelRidge
R²-------------------------------------------0.82
Mean Absolute Error---------------------0.52
Mean Square Error-----------------------0.5
Root Mean Square Error-----------------------0.71
Execution Time-------------------------------8.21
##################################################
Algorithme testé-------------- KNeighborsRegressor
R²-------------------------------------------0.42
Mean Absolute Error---------------------0.94
Mean Square Error-----------------------1.59
Root Mean Square Error-----------------------1.26
Execution Time-------------------------------9.43
##################################################
Algorithme testé-------------- DummyRegressor
R²--------------------------------------------0.01
Mean Absolute Error---------------------1.35
Mean Square Error-----------------------2.75
Root Mean Square Error-----------------------1.66
Execution Time-------------------------------0.0
##################################################
###########################################################################
Nous avons filtré cette fois-ci notre jeu de données en ne conservant
que les bâtiments qui ont un ENERGYSTARScore renseigné.
Nous observons que le fait d’avoir filtré les données et dans le même temps
retiré certaines valeurs extrêmes de notre jeu de données a tout logiquement
optimisé nos scores, même sans utilisation de la variable ENERGYSTARScore.
Cependant, les scores nous indiquent que l'ajout de la variable ENERGYSTARScore
améliore la prédiction de tous nos modèles, à la fois pour prédire la target
TotalGHGEmissions mais également SiteEnergyUse.
Voici un résumé des scores des 2 meilleurs algorithmes
**sans** et **avec** utilisation de l'ENERGYSTARScore :
Ces résultats confirment l'utilité de la variable ENERGYSTARScore.
Cependant, son apport, bien que non négligeable dans la qualité de prédiction
de nos modèles, ne démontre pas une nécessité d'utilisation si on prend en compte
la difficulté qu'ont les agents pour obtenir cette information avec les méthodes actuelles.
De plus, selon le temps, et l'énergie consacrée à obtenir cette donnée,
il peut être plus rentable de s'en passer compte tenu des bonnes performances de nos modèles.